A method for diagnosing intermittent arc type ground faults in a medium voltage distribution network

Abstract

The application relates to a kind of intermittent arc type ground fault diagnosis methods suitable for medium voltage distribution network, upload the time tag of fault time power frequency current, power frequency voltage, travelling wave current and travelling wave voltage array;Array is grouped and bundled;Waveform group is marked;The similarity between all travelling wave current arrays in the waveform group of fault target is determined respectively, and the similarity between all travelling wave voltage arrays is determined, and the two groups with the maximum similarity are taken as the travelling wave current signal and the travelling wave voltage signal of fault time respectively;Judge the validity of the travelling wave current signal and the travelling wave voltage signal of fault time;According to wavelet transform, the fault point is automatically identified, combined with line basic information, the arc type fault point is positioned;Form a positioning result set;Judge accurate fault point.The beneficial effect is: greatly improve the detection efficiency of intermittent arc type fault of distribution network, realize the automatic research and judgment function of intermittent arc type fault, shorten the outage time of line after fault, and be reliable in operation.

Description

Technical Field

[0001] The present invention relates to the technical field of arc-type grounding fault positioning in a medium-voltage distribution network, and in particular to a diagnosis method for intermittent arc-type grounding faults in a medium-voltage distribution network. Background Art

[0002] As a crucial link in the power system directly facing users, the operational reliability of the distribution network is a matter of widespread concern. Due to factors such as the environment and structure of distribution network lines, failures are inevitable. According to incomplete statistics, single-phase grounding faults in overhead distribution lines account for over 80% of these faults. These faults are primarily caused by lightning strikes, foreign objects hanging on the wires, and reduced insulation performance of hardware. Arcing faults are a common type of single-phase grounding fault. Arcing faults in distribution networks are caused by arcing. Arcing is a discharge phenomenon caused by current passing through air or insulating materials, which can cause fires, equipment damage, and even casualties.

[0003] Currently, detection of abnormal distribution network conditions largely relies on distribution automation systems or manual inspection techniques. Distribution automation systems utilize power frequency analysis to identify faults, effectively addressing metallic grounding and some non-metallic grounding faults. Manual inspections involve line management teams carrying drones, infrared temperature sensors, and other devices to conduct inspections according to inspection plans. The combined application of these technologies can improve the efficiency and accuracy of overhead line fault inspections, reduce maintenance costs, and enhance the reliability and safety of distribution systems. With continued technological advancements, distribution network overhead line fault inspection technology will continue to develop, providing more intelligent and efficient solutions for distribution system operations.

[0004] Of the two methods mentioned above, manual inspection is a regular patrol method that relies on manual personnel carrying instruments to conduct on-site line surveys. The period is long, and it is usually difficult to detect line faults in a timely manner, resulting in long-term line outages. First, the manual inspection method relies heavily on manual experience and can effectively troubleshoot some trace faults. However, since arc-type faults have the characteristics of intermittent breakdown, their discharge is an occasional phenomenon. Regular inspections are likely to fail to locate the fault, resulting in prolonged fault time and adverse consequences. Secondly, according to the distribution network automation fault analysis mechanism, the distribution network automation system can basically accurately sense and locate metallic grounding faults, but it is difficult for the distribution network automation system to sense arc-type faults caused by high-resistance grounding faults. In addition, since the zero-sequence current amplitude generated by arc-type faults is small, the distribution network automation system has a low recognition accuracy, which can easily lead to omissions in arc-type fault detection or reduced positioning accuracy.

[0005] In recent years, traveling wave positioning technology has been widely used in power systems. Traveling waves have the advantages of high detection sensitivity and wide applicability, and can effectively detect intermittent arc faults. However, due to the characteristics of traveling wave data, the traveling wave positioning method has the characteristics of complex data processing and large data volume. There is currently no effective means to analyze and process the data to realize the diagnosis of intermittent arc faults.

[0006] To address the need for intermittent arc fault detection in medium-voltage distribution networks, it is necessary to combine manual inspections with distribution network automation systems to solve the following problems:

[0007] 1) The problem of low timeliness of fault detection in manual inspection mode

[0008] Manual inspection is an offline detection method, usually carried out periodically or when needed, and its timeliness cannot be guaranteed. With the rapid development of information technology, online monitoring methods are also being widely promoted and applied, which also puts forward higher requirements for the timeliness of distribution network fault detection. Therefore, it is necessary to rely on information technology to propose an online monitoring method to achieve the function of timely detection of arc faults.

[0009] 2) Difficulty in processing data from traveling wave monitoring arc faults

[0010] Fault monitoring methods that rely on traveling wave dual-terminal positioning have been widely used in power grids, especially transmission networks. However, the distribution network environment is complex. Compared with instantaneous faults, arc faults are long-term processes, with a high number of faults and a large amount of traveling wave data. Relying on traveling wave monitoring terminals will collect a large amount of traveling wave data. How to reliably select valid data from this massive amount of data is one of the difficulties to be solved. It is necessary to propose a data screening and processing method to achieve reliable analysis and judgment of arc-type grounding faults.

[0011] 3) Traveling wave data often relies on manual diagnosis

[0012] The distribution network topology is complex, and distribution network fault analysis based on traveling wave dual-end positioning usually relies on manual diagnosis. That is, the data uploaded by the traveling wave monitoring terminal relies on manual analysis, and the final output of the analysis results requires high diagnostic capabilities of the diagnostic personnel. In addition, manual diagnosis is generally time-consuming, and it often takes half an hour to output the results, which is not conducive to the rapid restoration of power supply on the line. Summary of the Invention

[0013] The technical problem to be solved by the present invention is to provide a diagnostic method for intermittent arc-type grounding faults in a medium-voltage distribution network, so as to overcome the deficiencies in the above-mentioned prior art.

[0014] The present invention solves the above technical problems with the following technical solution: A method for diagnosing intermittent arc-type ground faults in a medium-voltage distribution network, comprising the following steps:

[0015] Step 1: The traveling wave monitoring terminal collects electrical signals on the line at all times, analyzes and processes the signals, and then uploads the power frequency current, power frequency voltage, traveling wave current, and traveling wave voltage arrays with a time stamp at the time of the fault.

[0016] Step 2: Group and bundle the uploaded arrays based on time periods to obtain waveform groups;

[0017] Step 3: Label the waveform group and at least determine the fault label;

[0018] Step 4: Determine the similarity between all traveling wave current arrays and the similarity between all traveling wave voltage arrays in the fault target waveform group, and select the two groups with the largest similarity as the traveling wave current signal and the traveling wave voltage signal at the fault moment, respectively.

[0019] Step 5: Determine the validity of the traveling wave current signal and the traveling wave voltage signal at the time of the fault. If both are determined to be valid, proceed to Step 7. If one of the two waveform groups is invalid, that group of waveforms is discarded and proceed to Step 6. If all waveform groups are invalid, all waveforms marked as faults in that group are discarded and the process ends.

[0020] Step 6: Select the traveling wave signal with the next highest absolute value of similarity in Step 4 as a replacement and repeat Step 5;

[0021] Step 7: Automatically identify the fault point based on wavelet transform and locate the arc fault point based on basic line information;

[0022] Step 8: Repeat the above steps to obtain multiple positioning results and form a positioning result set , the total number of elements in set P is ;

[0023] Step 9: There is a certain positioning element in the positioning result set P The highest frequency of occurrence, if the positioning result element The frequency of occurrence meets , where G is an element Frequency of occurrence, is the frequency of occurrence of other elements, then is the final location result of the arc fault. Otherwise, continue to accumulate the location result set P until the above conditions are met.

[0024] On the basis of the above technical solution, the present invention can also be improved as follows.

[0025] Further, Step 1 is specifically as follows:

[0026] Set the self-test power frequency current amplitude of the traveling wave monitoring terminal , self-check power frequency voltage amplitude , self-test traveling wave current amplitude and self-test traveling wave voltage amplitude ;

[0027] The electrical signals collected by the traveling wave monitoring terminal in real time on the line are: power frequency current array , power frequency voltage array , traveling wave current array and traveling wave voltage array ;

[0028] The judgment conditions are set according to the set amplitude, and the arrays collected in real time are judged. If any of the set judgment conditions is met, the power frequency current array, power frequency voltage array, traveling wave current array and traveling wave voltage array with time stamps at the same moment are uploaded.

[0029] Furthermore, the judgment conditions are set as follows:

[0030] Condition ①: Continuous The absolute value of the sampling point value is greater than ;

[0031] Condition ②: Continuous The absolute value of the sampling point value is in the interval ;

[0032] Condition ③: Continuous The absolute value of the sampling point value is greater than ;

[0033] Condition ④: Continuous The absolute value of the sampling point value is greater than .

[0034] Going further, The value is 5. The value is 5. The value is 10 and The value is 10.

[0035] Further, Step 2 is specifically as follows:

[0036] The GPS time information of the first uploaded array As a reference point, wait min later, The arrays uploaded within min are grouped and processed based on GPS time. All arrays within a section are bundled into a group to obtain a waveform group.

[0037] Furthermore, the rules for determining the fault target in Step 3 are as follows:

[0038] Fault mark: Mark the waveform group that meets conditions ②, ③, and ④ at the same time as the fault waveform group.

[0039] Furthermore, the standards determined in Step 3 also include: load increase standard, power outage standard, power supply standard and others;

[0040] Determine the load increase mark, power outage mark, power supply mark and others according to the following rules:

[0041] Load increase mark: Mark the waveform group that meets condition ① but does not meet condition ② as a load increase waveform group and send a load increase SMS;

[0042] Power outage mark: The waveform group that meets condition ② but does not meet conditions ① and ③ will be marked as a power outage waveform group and a power outage SMS will be sent;

[0043] Power transmission mark: The waveform group that meets both conditions ① and ② but does not meet conditions ③ or ④ will be marked as a power transmission waveform group, and a power transmission SMS will be sent;

[0044] Others: The waveform groups excluding the above scenarios will be discarded directly.

[0045] Going further, The value is 5.

[0046] Further, Step 4 is as follows:

[0047] Step 4-1: Assume that the two traveling wave current arrays in the fault waveform group are and ;

[0048] Step 4-2: Calculate the initial state waveform group and Similarity , the calculation formula is as follows:

[0049]

[0050] in For arrays The mathematical mean of the sampling points, For arrays The mathematical mean of the sampling points;

[0051] Step 4-3: Group the waveforms The end sampling point moves to the front end to form a new waveform group ;

[0052] Step 4-4: Calculate the waveform group With Waveform Group Similarity ;

[0053] Step4-5: Repeat Step4-3 and Step4-4 to group the waveforms Finish Reorganization, obtain The similarities are: 、 、 … ;

[0054] Step 4-6: Take the similarity with the largest absolute value As a waveform group With Waveform Group The final similarity is the traveling wave current signal at the moment of fault;

[0055] Similarly, the similarity between the two traveling wave voltage arrays in the fault mark waveform group is calculated, and the two waveform groups with the largest similarity are taken as the traveling wave voltage signals at the fault moment.

[0056] Furthermore, the validity judgment method in Step 5 is as follows:

[0057] The traveling wave current array is , array Each sampling point has corresponding GPS time stamp information;

[0058] Assume that the selected initial time of the traveling wave current includes year, month, day, hour, minute, second, millisecond, microsecond, and nanosecond, and the millisecond time of the waveform is intercepted as the reference time ;

[0059] Based on the benchmark time , select the power frequency current waveform corresponding to the moment, take the power frequency current peak point, the time corresponding to this point is ;

[0060] The traveling wave current array is processed as , the time scale range The values ​​of the sampling points outside are set to zero to obtain a new traveling wave current array The time interval between two sampling points is ;

[0061] Calculate the traveling wave current array The waveform energy is expressed as:

[0062]

[0063] Calculate the traveling wave current array The waveform energy is expressed as:

[0064]

[0065] like , then the traveling wave current array is valid, otherwise, it is invalid;

[0066] The same goes for traveling wave voltage. Select the power frequency voltage signal as a reference and repeat the above steps to determine whether it is effective.

[0067] The beneficial effects of the present invention are:

[0068] 1) Compared with existing manual inspections, the online monitoring method proposed in this invention can significantly improve the detection efficiency of intermittent arc faults in distribution networks, realize the automatic analysis and judgment function of intermittent arc faults, and shorten the power outage time of the line after the fault;

[0069] 2) Compared with the distribution network automation system, the method mentioned in the present invention can effectively make up for the detection capability of the distribution network automation system for high-resistance arc faults and operate reliably;

[0070] 3) Compared with the traveling wave positioning method based on manual analysis, the present invention provides a fully automatic diagnostic logic that can realize autonomous analysis of data and improve the level of automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of the method for diagnosing intermittent arc-type grounding faults in a medium-voltage distribution network according to the present invention;

[0072] Figure 2 This is the logic diagram for the analysis and processing of the line electrical signals collected at all times by the traveling wave monitoring terminal;

[0073] Figure 3 Label the logic diagram for the waveform group. DETAILED DESCRIPTION

[0074] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0075] Example 1

[0076] like Figure 1 As shown, a diagnostic method for intermittent arc-type ground faults in a medium-voltage distribution network includes the following steps:

[0077] Step 1: The traveling wave monitoring terminal collects electrical signals on the line at all times, analyzes and processes the signals, and then uploads the power frequency current array, power frequency voltage array, traveling wave current array, and traveling wave voltage array with a time stamp at the time of the fault.

[0078] Step2: Grouping and bundling the uploaded array according to the time period to obtain a waveform group;

[0079] Step3: Marking the waveform group and determining at least a fault mark;

[0080] Step4: Determining the similarity between all pairs of traveling wave current arrays and the similarity between all pairs of traveling wave voltage arrays in the waveform group of the fault mark, respectively, and taking the two groups with the highest similarity as the fault time traveling wave current signal and the fault time traveling wave voltage signal, respectively;

[0081] Step5: Determine the validity of the fault time traveling wave current signal and the fault time traveling wave voltage signal, respectively. If both are determined to be valid, proceed to Step7. If one of the two groups of waveforms is invalid, discard the group of waveforms and proceed to Step6. If all groups of waveforms are invalid, discard all groups of waveforms marked with the fault mark and end;

[0082] Step6: Select the traveling wave signal with the second highest similarity in Step4 to replace it and repeat Step5;

[0083] Step7: Automatically identify the fault point according to wavelet transform, and locate the arc-type fault point combined with the basic information of the line, and locate the tower as This method is a mature scheme, so it will not be repeated.

[0084] Step8: Repeat the above steps to obtain multiple positioning results to form a positioning result set The total number of elements in the set P is ;

[0085] Step9: There is a positioning element in the positioning result set P with the highest frequency. If the frequency of the positioning result element satisfies , where G is the frequency of the element , and is the frequency of other elements, respectively, then is the final positioning result of the arc-type fault. Otherwise, continue to accumulate the positioning result set P until the above conditions are met.

[0086] Example 2

[0087] As shown in Figure 2 , this embodiment is a further refinement based on Example 1, as follows:

[0088] Step1 is specifically:

[0089] Set the self-test power frequency current amplitude of the traveling wave monitoring terminal , self-check power frequency voltage amplitude , self-test traveling wave current amplitude and self-test traveling wave voltage amplitude ;

[0090] The electrical signals collected by the traveling wave monitoring terminal in real time on the line are: power frequency current array , power frequency voltage array , traveling wave current array and traveling wave voltage array ;

[0091] According to the set amplitude setting judgment conditions, the traveling wave monitoring terminal installed on the line judges the array collected in real time, that is, the traveling wave monitoring terminal has a self-test function. If any of the set judgment conditions is met, the power frequency current array, power frequency voltage array, traveling wave current array and traveling wave voltage array with time stamps at the same time will be uploaded. The data uploaded by the traveling wave monitoring terminal is received by the server.

[0092] Going further: Condition ①: Continuous The absolute value of the sampling point value is greater than ;

[0093] Condition ②: Continuous The absolute value of the sampling point value is in the interval ;

[0094] Condition ③: Continuous The absolute value of the sampling point value is greater than ;

[0095] Condition ④: Continuous The absolute value of the sampling point value is greater than .

[0096] The preferred value is 5. The preferred value is 5. The preferred value is 10. The preferred value is 10.

[0097] Example 3

[0098] This embodiment is a further refinement based on the embodiment 2, and is specifically as follows:

[0099] The uploaded arrays are all time-stamped, that is, each sampling point of the array data has corresponding time information. Considering the latency of terminal upload, Step 2 in this embodiment is specifically as follows:

[0100] The GPS time information of the first uploaded array (in milliseconds) as the reference point, wait min later, The arrays uploaded within min are grouped and processed based on GPS time. All arrays in the segment are bundled into a group to obtain a waveform group, where The preferred value is 5.

[0101] Example 4

[0102] like Figure 3 As shown, this embodiment is a further refinement based on Example 3, specifically as follows:

[0103] After grouping according to the above steps, the grouped waveform group contains a large amount of power frequency and traveling wave data. The fault mark is determined according to the following rules:

[0104] Fault mark: Mark the waveform group that meets conditions ②, ③, and ④ at the same time as the fault waveform group.

[0105] Furthermore: the standards determined in Step 3 also include: load increase standard, power outage standard, power supply standard and others;

[0106] Determine the load increase mark, power outage mark, power supply mark and others according to the following rules:

[0107] Load increase mark: Mark the waveform group that meets condition ① but does not meet condition ② as a load increase waveform group, and send a load increase SMS;

[0108] Power outage mark: The waveform group that meets condition ② but does not meet conditions ① and ③ will be marked as a power outage waveform group and a power outage SMS will be sent;

[0109] Power transmission mark: The waveform group that meets both conditions ① and ② but does not meet conditions ③ or ④ will be marked as a power transmission waveform group, and a power transmission SMS will be sent;

[0110] Others: The waveform groups excluding the above scenarios will be discarded directly.

[0111] Example 5

[0112] This embodiment is a further refinement based on embodiment 3 or 4, and is specifically as follows:

[0113] Step 4 is as follows:

[0114] Step 4-1: Assume that the two traveling wave current arrays in the fault waveform group are and ;

[0115] Step 4-2: Calculate the initial state waveform group and Similarity , the calculation formula is as follows:

[0116]

[0117] in For arrays The mathematical mean of the sampling points, For arrays The mathematical mean of the sampling points;

[0118] Step 4-3: Group the waveforms The end sampling point moves to the front end to form a new waveform group ;

[0119] Step 4-4: Calculate the waveform group With Waveform Group Similarity ;

[0120] Step4-5: Repeat Step4-3 and Step4-4 to group the waveforms Finish Reorganization, obtain The similarities are: 、 、 … ;

[0121] Step 4-6: Take the similarity with the largest absolute value As a waveform group With Waveform Group The final similarity is the traveling wave current signal at the fault moment;

[0122] Similarly, the similarity between the two traveling wave voltage arrays in the fault mark waveform group is calculated, and the two waveform groups with the largest similarity are taken as the traveling wave voltage signals at the fault moment.

[0123] Example 6

[0124] This embodiment is a further refinement based on any one of Embodiments 1 to 5, and is specifically as follows:

[0125] The validity judgment method in Step 5 is as follows:

[0126] Taking traveling wave current as an example, the traveling wave current array is , array Each sampling point has corresponding GPS time stamp information;

[0127] Assume that the selected initial time of the traveling wave current includes year, month, day, hour, minute, second, millisecond, microsecond, and nanosecond, and the millisecond time of the waveform is intercepted as the reference time ;

[0128] Based on the benchmark time , select the power frequency current waveform corresponding to the moment, take the power frequency current peak point, the time corresponding to this point is ;

[0129] The traveling wave current array is processed as , the time scale range The values ​​of the sampling points outside are set to zero to obtain a new traveling wave current array The time interval between two sampling points is ;

[0130] Calculate the traveling wave current array The waveform energy is expressed as:

[0131]

[0132] Calculate the traveling wave current array The waveform energy is expressed as:

[0133]

[0134] like , then the traveling wave current array is valid, otherwise, it is invalid;

[0135] The same goes for traveling wave voltage. Select the power frequency voltage signal as a reference and repeat the above steps to determine whether it is effective.

[0136] According to the above steps, the effective arc fault traveling wave signal can be screened from the massive data and the double-end positioning process can be entered, that is, Step 7.

[0137] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for diagnosing intermittent arc-type ground faults in a medium-voltage distribution network, characterized in that: The steps include: Step 1: The traveling wave monitoring terminal collects electrical signals on the line at all times, analyzes and processes the signals, and then uploads the power frequency current, power frequency voltage, traveling wave current, and traveling wave voltage arrays with a time stamp at the time of the fault. Step 2: Group and bundle the uploaded arrays based on time periods to obtain waveform groups; Step 3: Label the waveform group and at least determine the fault label; Step 4: Determine the similarity between all traveling wave current arrays and the similarity between all traveling wave voltage arrays in the fault target waveform group, and select the two groups with the largest absolute value of similarity as the traveling wave current signal and the traveling wave voltage signal at the fault moment, respectively. Step 5: Determine the validity of the traveling wave current signal and the traveling wave voltage signal at the time of the fault. If both are determined to be valid, proceed to Step 7. If one of the two waveform groups is invalid, that group of waveforms is discarded and proceed to Step 6. If all waveform groups are invalid, all waveforms marked as faults in that group are discarded and the process ends. Step 6: Select the traveling wave signal with the next highest absolute value of similarity in Step 4 as a replacement and repeat Step 5; Step 7: Automatically identify the fault point based on wavelet transform and locate the arc fault point based on basic line information; Step 8: Repeat the above steps to obtain multiple positioning results and form a positioning result set , the total number of elements in set P is ; Step 9: There is a certain positioning element in the positioning result set P The highest frequency of occurrence, if the positioning element The frequency of occurrence meets , where G is an element Frequency of occurrence, is the frequency of occurrence of other elements, then is the final location result of the arc fault. Otherwise, continue to accumulate the location result set P until the above conditions are met.

2. A method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to claim 1, characterized in that: Step 1 is as follows: Set the self-test power frequency current amplitude of the traveling wave monitoring terminal , self-check power frequency voltage amplitude , self-test traveling wave current amplitude and self-test traveling wave voltage amplitude ; The electrical signals collected by the traveling wave monitoring terminal in real time on the line are: power frequency current array , power frequency voltage array , traveling wave current array and traveling wave voltage array ; The judgment conditions are set according to the set amplitude, and the arrays collected in real time are judged. If any of the set judgment conditions is met, the power frequency current array, power frequency voltage array, traveling wave current array and traveling wave voltage array with time stamps at the same moment are uploaded.

3. The method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to claim 2, characterized in that: The judgment conditions are: Condition ①: Continuous The absolute value of the sampling point value is greater than ; Condition ②: Continuous The absolute value of the sampling point value is in the interval ; Condition ③: Continuous The absolute value of the sampling point value is greater than ; Condition ④: Continuous The absolute value of the sampling point value is greater than .

4. A method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to claim 3, characterized in that: The value is 5. The value is 5. The value is 10 and The value is 10.

5. The method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to claim 3, characterized in that: Step 2 is as follows: The GPS time information of the first uploaded array As a reference point, wait min later, The arrays uploaded within min are grouped and processed based on GPS time. All arrays within a section are bundled into a group to obtain a waveform group.

6. A method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to claim 5, characterized in that: The rules for determining the fault target in Step 3 are as follows: Fault mark: Mark the waveform group that meets conditions ②, ③, and ④ at the same time as the fault mark waveform group.

7. A method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to claim 5, characterized in that: The standards determined in Step 3 also include: load increase standard, power outage standard, power supply standard and others; Determine the load increase mark, power outage mark, power supply mark and others according to the following rules: Load increase mark: Mark the waveform group that meets condition ① but does not meet condition ② as a load increase waveform group and send a load increase SMS; Power outage mark: The waveform group that meets condition ② but does not meet conditions ① and ③ will be marked as a power outage waveform group and a power outage SMS will be sent; Power transmission mark: The waveform group that meets both conditions ① and ② but does not meet conditions ③ or ④ will be marked as a power transmission waveform group, and a power transmission SMS will be sent; Others: The waveform groups excluding the above scenarios will be discarded directly.

8. The method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to claim 5, characterized in that: The value is 5.

9. A method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to any one of claims 1 to 8, characterized in that: Step 4 is as follows: Step 4-1: Assume that the two traveling wave current arrays in the fault waveform group are and ; Step 4-2: Calculate the initial state waveform group and Similarity , the calculation formula is as follows: in For arrays The mathematical mean of the sampling points, For arrays The mathematical mean of the sampling points; Step 4-3: Group the waveforms The end sampling point moves to the front end to form a new waveform group ; Step 4-4: Calculate the waveform group With Waveform Group Similarity ; Step4-5: Repeat Step4-3 and Step4-4 to group the waveforms Finish Reorganization, obtain The similarities are: 、 、 … ; Step 4-6: Take the similarity with the largest absolute value As a waveform group With Waveform Group The final similarity of the corresponding waveform group With Waveform Group That is the traveling wave current signal at the moment of fault; Similarly, the similarity between the two traveling wave voltage arrays in the fault mark waveform group is calculated, and the two waveform groups with the largest absolute value of similarity are taken as the traveling wave voltage signals at the fault moment.

10. A method for diagnosing intermittent arc-type ground faults in a medium voltage distribution network according to any one of claims 1 to 8, characterized in that: The validity judgment method in Step 5 is as follows: The traveling wave current array is , array Each sampling point has corresponding GPS time stamp information; Assume that the selected initial time of the traveling wave current includes year, month, day, hour, minute, second, millisecond, microsecond, and nanosecond, and the millisecond time of the waveform is intercepted as the reference time ; Based on the benchmark time , select the power frequency current waveform corresponding to the moment, take the power frequency current peak point, the time corresponding to this point is ; Processing traveling wave current arrays , the time scale range The values ​​of the sampling points outside are set to zero to obtain a new traveling wave current array The time interval between two sampling points is ; Calculate the traveling wave current array The waveform energy is expressed as: Calculate the traveling wave current array The waveform energy is expressed as: like , then the traveling wave current array is valid, otherwise, it is invalid; The same goes for traveling wave voltage. Select the power frequency voltage signal as a reference and repeat the above steps to determine whether it is effective.

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