Power distribution network line power failure positioning method and system based on intelligent distributed ranging

Abstract

The application discloses a power distribution network line power failure positioning method and system based on intelligent distributed ranging, belongs to the technical field of power grid fault detection, and comprises the following steps: S10, a preset line historical fault positioning point is acquired, the preset line is divided into a plurality of line sections, and line sections corresponding to historical fault positioning points are acquired; and S20, based on the acquired line sections corresponding to the historical fault positioning points, the historical fault occurrence frequency of each line section is counted, and fault coordinates corresponding to any fault are acquired; by segmenting and collecting fault points of a preset power distribution network line, the segmented line can be distinguished according to the fault occurrence frequency, so that when a fault occurs in a future period, the fault positioning of the distinguished high-fault-rate line section can be more concentrated, the efficiency of fault positioning is improved, the corresponding infrared thermal imaging data is acquired, the accuracy of fault point positioning is improved, and the efficiency of fault point positioning is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid fault detection, in particular to a power distribution network line power supply fault positioning method and system based on intelligent distributed ranging. BACKGROUND

[0002] As the supply and demand link connecting the power transmission network and power users, the power distribution network has the characteristics of varied line structure and complex fault conditions. Among them, wire short circuit is one of the common faults in electrical systems, which may cause circuit interruption, equipment damage and even safety risks such as fire. To solve this problem, the key lies in tracking and locating the short circuit fault point. For tracking and locating the fault point of the power distribution network line, a power distribution network line fault positioning method and system with technical application number CN202010081955.X is provided. The feature of this technical application is that when single-phase ground fault and latent fault occur in the power distribution network, impulse voltage signals will be generated, and the local partial discharge detection sensor closest to the fault point will first detect the impulse voltage signals. Then the time and corresponding position of the first detection of the impulse signal are analyzed, and then the fault point position is calculated, i.e. the fault positioning is completed. It can quickly and accurately realize online ranging and fault positioning, and effectively improve the power supply reliability of the power grid.

[0003] Another technical application number CN202211541520.4 provides a power distribution network fault positioning protection method and system containing distributed power supply. The technical solution includes determining the fault range based on the power frequency current mutation amplitude of each line in the power distribution network, and comparing the current phase or amplitude of each branch in the fault range to determine the fault line. This technical solution can reduce the calculation amount on irrelevant branches, reduce the calculation amount, and reduce the data communication pressure. It can quickly locate the fault line, then perform protection action on the fault line, effectively protect the operation of the power distribution network containing distributed power supply, and avoid the power supply safety hazards caused by not timely handling the fault.

[0004] In real life, the methods for locating the line fault in the power system are various, but the principles of positioning are to measure the resistance or voltage amplitude of the fault area on the cable or wire, combine the parameters of the cable or wire, and then calculate and locate the fault position. However, in the actual application of calculation and positioning, there are many deficiencies. Among them, due to the diversity and complexity of the power distribution network line, the positioning effect of the current ground fault corresponding to the power supply fault of the power grid line is not ideal, sometimes leading to positioning errors, thereby increasing the number of fault positioning operations and reducing the efficiency of wire maintenance. SUMMARY

[0005] In view of the problems existing in the above-mentioned existing power grid fault detection technology field, the present application is proposed.

[0006] Therefore, one of the purposes of the present application is to provide a power distribution network line power failure positioning method and system based on intelligent distributed ranging, which can distinguish the segmented line according to the number of fault occurrences, so that when a fault occurs in the future period, the high-fault-rate line segment can be more concentratedly positioned, thereby improving the efficiency of fault positioning. At the same time, in the corresponding line segment, by giving two interval distance farthest fault positioning points, when a fault occurs in the future period, the maintenance personnel can collect the fault point between the two interval distance farthest fault positioning points to obtain the corresponding infrared thermal imaging data, which not only improves the accuracy of fault positioning, but also further improves the efficiency of fault positioning.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In one aspect, the present application provides a power distribution network line power failure positioning method based on intelligent distributed ranging, comprising the following steps:

[0009] Step S10: Obtain the historical fault positioning points of the preset line, divide the line into several line segments, and obtain the line segment corresponding to each historical fault positioning point;

[0010] Step S20: Based on the obtained line segment corresponding to each historical fault positioning point, count the number of historical faults of each line segment, and obtain the fault coordinates corresponding to any one fault;

[0011] Step S30: Count the number of fault coordinates, and distinguish the segmented line based on the counted number of fault coordinates, the distinguishing method including distinguishing the line segment into a high-fault-rate line segment, a medium-fault-rate line segment and a low-fault-rate line segment, and based on the high-fault-rate line segment, collecting the current value of the initial point of the preset line when a fault occurs, and collecting the current value from the current value to the current value of the line segment corresponding to the fault coordinates based on the current value, to generate a current value coefficient Α R ;

[0012] Step S40: In the current value coefficient Α R , at least 10 current data are collected according to the distance from the initial point of the preset line to the corresponding line segment, the current data with the most occurrences is analyzed to the corresponding line segment, and the current data variation law of the fault point to the corresponding line segment is analyzed based on the current data;

[0013] Step S50: when the change rule is that the current data shows an increasing trend, then the most frequently occurring current data of the corresponding line segment is used to set a safety threshold value; when the current data of the preset line from the initial point to the corresponding line segment is consistent with the safety threshold value in the future period, it is determined that the current of the line segment will increase, and a fault warning is issued; otherwise, it is not determined.

[0014] As a preferred scheme of the present application, in the step S10, the preset line is divided into at least 5 line segments, which are marked as L1 segment, L2 segment, L3 segment, L4 segment and L5 segment, and the current values at the time of historical fault occurrence of each line segment are collected, and the infrared thermal imaging data corresponding to the fault positioning point in the corresponding line segment is obtained according to the current values.

[0015] As a preferred scheme of the present application, in the step S40, the current data is collected according to the distance from the initial point of the preset line to the corresponding line segment, and the collection method includes:

[0016] When the distance from the initial point of the preset line to the corresponding line segment is 1-3 km, the number of collected current data is ≥10;

[0017] When the distance from the initial point of the preset line to the corresponding line segment is 3-5 km, the number of collected current data is ≥13;

[0018] When the distance from the initial point of the preset line to the corresponding line segment is 5-7 km, the number of collected current data is ≥16;

[0019] And in different current data collection numbers, the first 3 current data are used to obtain the confidence of the occurrence of fault of different line segments in the future period, wherein the first 3 current data are marked as independent group current data, and the confidence is calculated according to the following formula:

[0020] Wherein, wj represents the wth independent group current data collected for the jth time in the line segment;

[0021] In the formula, H represents that every ten times of fault is a current data acquisition period of the independent group, and the same independent group current data appears in the acquisition period; λ represents the number of different independent group current data appearing in the acquisition period, and G x represents the Gth different independent group current data obtained for the xth time in the acquisition period;

[0022] When the number of the same independent group current data appearing in the acquisition period is ≤5 times, it is determined that the confidence is low, otherwise, it is not determined.

[0023] As a preferred scheme of the present application, wherein: in the acquisition period, the same independent group current data is used to analyze the change rule of three current data contained therein, and the median current data between the first two current data is analyzed, when the median current data between the first two current data is inconsistent with the median current data in the acquisition period, it is determined that the corresponding line segment fault occurrence probability is low, otherwise, it is not determined.

[0024] As a preferred scheme of the present application, wherein: in the acquisition period, the same independent group current data is used to analyze the change rule of three current data contained therein, and the median current data between the first two current data is analyzed, when the median current data between the first two current data is inconsistent with the median current data in the acquisition period, it is determined that the corresponding line segment fault occurrence probability is low, otherwise, it is not determined.

[0025] As a preferred scheme of the present application, wherein: in the acquisition period, the same independent group current data is used to analyze the change rule of three current data contained therein, and the median current data between the first two current data is analyzed, when the median current data between the first two current data is inconsistent with the median current data in the acquisition period, it is determined that the corresponding line segment fault occurrence probability is low, otherwise, it is not determined.

[0026] As a preferred scheme of the present application, wherein: in the acquisition period, the same independent group current data is used to analyze the change rule of three current data contained therein, and the median current data between the first two current data is analyzed, when the median current data between the first two current data is inconsistent with the median current data in the acquisition period, it is determined that the corresponding line segment fault occurrence probability is low, otherwise, it is not determined.

[0027] As a preferred scheme of the present application, wherein: in the acquisition period, the same independent group current data is used to analyze the change rule of three current data contained therein, and the median current data between the first two current data is analyzed, when the median current data between the first two current data is inconsistent with the median current data in the acquisition period, it is determined that the corresponding line segment fault occurrence probability is low, otherwise, it is not determined.

[0028] On the other hand, the present application provides a power distribution network line power fault positioning system based on intelligent distributed ranging, which is applied to a power distribution network line power fault positioning method based on intelligent distributed ranging, comprising:

[0029] The data acquisition module is configured to acquire historical fault positioning points of a preset line, divide the preset line into a plurality of line sections, and acquire line sections corresponding to the historical fault positioning points; and based on the acquired line sections corresponding to the historical fault positioning points, count the number of historical faults of each line section, and acquire fault coordinates corresponding to any one fault;

[0030] The data division module is configured to count the number of fault coordinates, and divide the divided line sections based on the counted number of fault coordinates.

[0031] The division manner includes dividing the line sections into a high-fault-rate line section, a medium-fault-rate line section, and a low-fault-rate line section, and based on the high-fault-rate line section, acquiring a current value of an initial point of the preset line when a fault occurs, and based on the current value, acquiring a current value from the current value to a current value of a line section corresponding to the fault coordinates, to generate a current value coefficient A R .

[0032] The fusion analysis unit is configured to, in the current value coefficient A R , acquire at least 10 current data from the initial point of the preset line to the corresponding line section according to the distance, analyze the current data that appears most frequently from the initial point to the corresponding line section, and analyze the current data variation law of the fault point from the initial point to the corresponding line section based on the current data; the fusion analysis unit includes a judgment module.

[0033] The judgment module is configured to, when the variation law is that the current data shows an increasing trend, preset a safety threshold based on the current data that appears most frequently from the initial point to the corresponding line section, and when the current data of the initial point of the preset line to the corresponding line section in a future period is consistent with the safety threshold, determine that the current of the line section will increase, and issue a fault warning, otherwise, do not determine.

[0034] The confidence degree calculation module is configured to, in response to the fusion analysis unit, acquire the confidence degree of the occurrence of a fault of each line section in a future period based on the first three current data in different current data acquisition numbers.

[0035] Advantages:

[0036] The application can distinguish the segmented lines according to the fault occurrence frequency, so that the high fault rate line segment can be more concentratedly positioned when a fault occurs in the future period, thereby improving the efficiency of fault positioning, and the fault point can be locked by tracking the current data corresponding to the historical fault in the corresponding line segment, so that the infrared thermal imaging data of the corresponding line segment can be collected faster, thereby the corresponding line segment is repaired, and in the corresponding line segment, by giving two fault positioning points with the farthest interval distance, when a fault occurs in the future period, the maintenance personnel can collect the fault point between the two fault positioning points with the farthest interval distance to obtain the infrared thermal imaging data corresponding to the fault point, thereby improving the accuracy of fault positioning and further improving the efficiency of fault positioning. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0038] Fig. 1 The modular structure diagram of the power distribution network line power fault positioning system based on intelligent distributed ranging of the embodiment of the present application;

[0039] Fig. 2 The method flow diagram of the embodiment of the present application;

[0040] The figure label: 110-data acquisition module; 120-data distinguishing module; 130-fusion analysis unit; 1301-determination module; 140-confidence calculation module. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme of the embodiments of the present application will be described clearly and completely in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0042] Due to many deficiencies in the actual application of the prior art in fault positioning calculation, the positioning effect of the current ground fault corresponding to the power supply fault of the distribution network line is not ideal due to the diversity and complexity of the distribution network line, sometimes leading to positioning errors, thereby increasing the number of fault positioning operations and reducing the efficiency of wire maintenance.

[0043] Based on this, the present application provides a distribution network line power supply fault positioning method and system based on intelligent distributed ranging, which can distinguish the segmented line according to the number of fault occurrences by segmenting the fault points of the preset distribution network line, so that when a fault occurs in the future period, the fault positioning of the distinguished high fault rate line segment can be more concentrated, thereby improving the efficiency of fault positioning.

[0044] The present application will be further described in detail below by way of examples and in conjunction with the accompanying drawings.

[0045] Reference Figs. 1-2 For an embodiment of the present application, the embodiment provides a distribution network line power supply fault positioning method based on intelligent distributed ranging, comprising the following steps:

[0046] Step S10: Obtain the historical fault positioning points of the preset line, divide the preset line into a plurality of line segments, and obtain the line segments corresponding to the historical fault positioning points;

[0047] It should be noted that the preset line is divided into a plurality of line segments, and the number of divisions is at least 5 or more. The 5 line segments are marked as L1 segment, L2 segment, L3 segment, L4 segment and L5 segment. The current value when each line segment has a historical fault is collected, and the infrared thermal imaging data corresponding to the fault positioning point in the corresponding line segment is obtained according to the current value;

[0048] According to the operation experience of the distribution network line, in the current ground fault, when a short circuit occurs, the current will increase, because the resistance in the circuit becomes smaller when a short circuit occurs, and the voltage remains unchanged. According to Ohm's law I=U / R, when the voltage U is constant, the decrease of the resistance R will cause the increase of the current I. When a short circuit occurs, the current provided by the power supply will be much larger than that provided by the path, resulting in that the current does not pass through the load but directly flows through the conductor, forming a closed loop. Therefore, the current value has practical significance as a reference;

[0049] Step S20: Based on the obtained line segments corresponding to the historical fault positioning points, count the number of historical faults of each line segment, and obtain the fault coordinates corresponding to any one fault;

[0050] It should be noted that the fault coordinates correspond to the fault positioning points.

[0051] Step S30: count the number of fault coordinates, and distinguish the divided line sections based on the counted number of fault coordinates, including distinguishing the line sections into high-fault-rate line sections, medium-fault-rate line sections and low-fault-rate line sections, collecting the current value of the preset line initial point when a fault occurs based on the high-fault-rate line sections, collecting the current value from the current value of the preset line initial point to the corresponding line section based on the current value, and generating a current value coefficient A R ;

[0052] Step S40: in the current value coefficient A R , collect at least 10 current data according to the distance from the preset line initial point to the corresponding line section , analyze the current data that appears most frequently to the corresponding line section, and analyze the current data change rule to the fault point of the corresponding line section based on the current data;

[0053] In this embodiment, the current data is collected according to the distance from the preset line initial point to the corresponding line section, and the collection method includes:

[0054] When the distance from the preset line initial point to the corresponding line section is 1-3 km, the number of collected current data is ≥10;

[0055] When the distance from the preset line initial point to the corresponding line section is 3-5 km, the number of collected current data is ≥13;

[0056] When the distance from the preset line initial point to the corresponding line section is 5-7 km, the number of collected current data is ≥16;

[0057] And in different current data collection numbers, the confidence of different line sections appearing faults in future time periods is obtained based on the first 3 current data, wherein the first 3 current data are marked as independent group current data, and the obtaining method is calculated according to the following formula:

[0058] Wherein, wj represents the wth independent group current data collected for the jth time in the line section;

[0059] In the formula, H represents that every ten times of faults is an independent group current data acquisition period, and the same independent group current data appears in the acquisition period; λ represents the number of different independent group current data appearing in the acquisition period, and G x represents the Gth different independent group current data obtained for the xth time in the acquisition period;

[0060] When the number of the same independent group current data appearing in the acquisition period is ≤5 times, it is determined that the confidence is low, otherwise it is not determined;

[0061] Further, the embodiment analyzes the change rule of the three current data contained in the same independent group current data in the acquisition period, and analyzes the median current data between the first two current data, and if the median current data between the first two current data is inconsistent with the median current data in the acquisition period, it is determined that the corresponding line segment has a low probability of fault, otherwise, it is not determined.

[0062] The embodiment also includes collecting the two interval distances farthest from the fault positioning point in the acquisition period based on the same independent group current data, and when the line segment fails in the future period, the infrared thermal imaging data within the interval distance is collected, and the interval distance is marked as the main collection area, and the interval distance outside is marked as the secondary collection area.

[0063] Meanwhile, the embodiment sorts the infrared thermal imaging data collected in the main collection area in order from low to high, analyzes the infrared thermal imaging data with the highest frequency in the sorted data, and marks the infrared thermal imaging data as the first infrared thermal imaging data, and when the infrared thermal imaging data of the corresponding line segment needs to be collected according to the fault positioning point in the future period, the first infrared thermal imaging data is collected as the target.

[0064] Further, the current data corresponding to the first infrared thermal imaging data is obtained, and the closest value greater than and less than the current data is obtained based on the current data, and when the first infrared thermal imaging data is collected based on the current data corresponding to the first infrared thermal imaging data and is not collected successfully in the future period, the closest value greater than and / or less than the current data is collected again.

[0065] On the basis of the above, the embodiment obtains the proportion of the infrared thermal imaging data collected successfully based on the closest value greater than and / or less than the current data, and when one of the greater than and / or less than occupies a large proportion, the one with a large proportion is taken as the closest value for re-collection.

[0066] Step S50: When the change rule is that the current data increases, the safety threshold is preset based on the current data with the highest frequency to the corresponding line segment, and when the current data of the line preset to the initial point of the corresponding line segment is consistent with the safety threshold in the future period, it is determined that the current of the line segment will increase, and a fault warning is issued, otherwise, it is not determined.

[0067] Based on the above, the application can distinguish the segmented lines according to the fault occurrence frequency, so that the high fault rate line segment can be more concentratedly positioned when a fault occurs in the future period, thereby improving the efficiency of fault positioning. Meanwhile, in the corresponding line segment, by giving two interval distance farthest fault positioning points, when a fault occurs in the future period, the maintenance personnel can collect the fault point between the two interval distance farthest fault positioning points to obtain the corresponding infrared thermal imaging data, which not only improves the accuracy of fault positioning, but also further improves the efficiency of fault positioning.

[0068] The embodiment combines the above power supply fault positioning method for distribution network line based on intelligent distributed ranging, and further proposes a working system applied to the method, as follows:

[0069] The data acquisition module 110 is configured to acquire historical fault positioning points of the preset line, divide the preset line into a plurality of line segments, and acquire line segments corresponding to each historical fault positioning point. Based on the acquired line segments corresponding to each historical fault positioning point, the historical fault occurrence frequency of each line segment is counted, and the fault coordinates corresponding to any fault are acquired.

[0070] The data division module 120 is configured to count the number of fault coordinates, and divide the segmented line segments based on the counted number of fault coordinates.

[0071] The division method includes dividing the line segments into high fault rate line segments, medium fault rate line segments and low fault rate line segments, and acquiring the current value of the initial point of the preset line when a fault occurs based on the high fault rate line segments. Meanwhile, the current value is acquired based on the current value to the current value of the line segment corresponding to the fault coordinates, and the current value coefficient A is generated. R ;

[0072] The fusion analysis unit 130 is configured to acquire at least 10 current data from the initial point of the preset line to the corresponding line segment based on the distance of the current value coefficient A R . The analysis unit 130 is configured to analyze the current data with the highest occurrence frequency from the initial point of the corresponding line segment, and analyze the current data variation law of the fault point of the corresponding line segment based on the current data. The fusion analysis unit 130 includes a determination module 1301.

[0073] The determination module 1301 is configured to, when the variation law is that the current data shows an increasing trend, preset a safety threshold based on the current data with the highest occurrence frequency from the initial point of the corresponding line segment. When the current data of the preset line from the initial point of the corresponding line segment in the future period is consistent with the safety threshold, it is determined that the current of the line segment will increase, and a fault warning is issued. Otherwise, it is not determined.

[0074] The confidence calculation module 140 is responsive to the fusion analysis unit 130 and is configured to obtain the confidence of the fault of the different line sections in the future period based on the first three current data in the different current data collection numbers.

[0075] In summary, the application can distinguish the segmented line according to the fault occurrence frequency by segmenting and collecting the fault points of the preset power distribution network line, so that the high fault rate line section can be more concentratedly positioned when a fault occurs in the future period, thereby improving the efficiency of fault positioning. In addition, the fault point can be locked by tracking the current data corresponding to the historical fault with the most occurrence times in the corresponding line section, so that the infrared thermal imaging data of the corresponding line section can be collected faster, thereby repairing the corresponding line section. Meanwhile, by giving two fault positioning points with the farthest interval distance in the corresponding line section, the maintenance personnel can collect the fault point between the two fault positioning points with the farthest interval distance to obtain the infrared thermal imaging data corresponding to the fault point when a fault occurs in the future period, thereby improving the accuracy of fault positioning and further improving the efficiency of fault positioning.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A power distribution network line power fault location method based on intelligent distribution ranging, characterized in that, The method comprises the following steps: Step S10: Obtain a preset line historical fault positioning point, divide the preset line into several line sections, and obtain the line sections corresponding to the historical fault positioning points; The preset line is divided into several line sections, and the number of divisions is at least 5 or more. The 5 line sections are marked as L1 section, L2 section, L3 section, L4 section, and L5 section. The current value of each line section when a historical fault occurs is collected, and the infrared thermal imaging data corresponding to the fault positioning point in the corresponding line section is obtained according to the current value; Step S20: Based on the obtained line sections corresponding to the historical fault positioning points, the number of historical faults of each line section is counted, and the fault coordinates corresponding to any one fault are obtained; Step S30: counting the fault coordinate number, and distinguishing the divided line section based on the counted fault coordinate number, the distinguishing manner including distinguishing the line section into high fault rate line section, medium fault rate line section and low fault rate line section, and collecting the current value of the preset line initial point when the fault occurs based on the high fault rate line section, and collecting the current value from the current value of the initial point to the current value of the line section corresponding to the fault coordinate based on the current value, to generate the current value coefficient A R ; Step S40: at the current value coefficient A R At least 10 current data are collected according to the distance from the initial point of the preset line to the corresponding line segment The current data with the highest occurrence frequency from the initial point to the corresponding line segment are analyzed, and the current data variation law from the initial point to the fault point of the corresponding line segment is analyzed based on the current data According to the distance from the initial point of the preset line to the corresponding line section, the current data is collected, and the collection method includes: When the distance from the initial point of the preset line to the corresponding line section is 1-3 km, the number of collected current data is ≥10; When the distance from the initial point of the preset line to the corresponding line section is 3-5 km, the number of collected current data is ≥13; When the distance from the initial point of the preset line to the corresponding line section is 5-7 km, the number of collected current data is ≥16; Among different numbers of current data collected, the confidence level of the occurrence of a fault in the future period in different line sections is obtained based on the first three current data, wherein the first three current data are marked as independent group current data, and the confidence level is calculated according to the following formula: wherein w j represents the wthindependent group of current data collected at the jthacquisition of the line segment; where H represents the number of occurrences of the same independent set of current data in each ten failure events; λ represents the number of occurrences of different independent sets of current data in each ten failure events; G x represents the Gth different independent set of current data acquired at the xth acquisition in the acquisition period. When the number of occurrences of the same independent group current data in the acquisition period is ≤5 times, it is determined that the confidence level is low, otherwise it is not determined; Step S50: When the change rule is that the current data shows an increasing trend, the current data with the highest number of occurrences from the initial point of the corresponding line section is used as the basis to set a safety threshold. When the current data from the initial point of the corresponding line section of the preset line in the future period is consistent with the safety threshold, it is determined that the current of the line section will increase, and a fault warning is issued, otherwise it is not determined.

2. The smart distribution-based distance relay based power fault location method for distribution network as claimed in claim 1 wherein, In the acquisition period, the change rule of the three current data contained in the same independent group current data is analyzed based on the same independent group current data, and the median current data between the first two current data is analyzed based on the first two current data. If the median current data between the first two current data in the independent group current data collected in the future period is inconsistent with the median current data in the acquisition period, it is determined that the fault occurrence probability of the corresponding line section is low, otherwise it is not determined.

3. The smart distribution-based line distance relaying method for locating the faulted section of a distribution feeder of claim 2, wherein, In the acquisition period, the two fault positioning points with the farthest interval distance among the fault positioning points appearing in the same independent group current data are collected based on the same independent group current data. When a fault occurs in the line section in the future period, the infrared thermal imaging data within the interval distance is collected, and the interval distance is marked as the main collection area, and the interval distance outside is marked as the secondary collection area.

4. The smart distribution-based line distance relaying method for locating the faulted section of a distribution feeder of claim 3, wherein, In the main acquisition area, the acquired infrared thermal imaging data is sorted in order from low to high, and in the sorted data, the infrared thermal imaging data with the highest occurrence frequency is analyzed and marked as the first infrared thermal imaging data. When infrared thermal imaging data of the corresponding line segment needs to be collected according to the fault positioning point in the future period, the first infrared thermal imaging data is collected as the target.

5. The smart distribution-based line distance protection method for locating the faulted section of the distribution network according to claim 4, wherein, The current data corresponding to the first infrared thermal imaging data is obtained, and the closest values greater and smaller than the current data are obtained based on the current data. When the first infrared thermal imaging data is collected based on the current data corresponding to the first infrared thermal imaging data and is not successfully collected in the future period, the closest values greater and / or smaller than the current data are re-collected based on the closest values.

6. The smart distribution-based distance relay based power fault location method for distribution network as claimed in claim 5 wherein, The proportion of successful collection of infrared thermal imaging data based on the closest values greater and / or smaller than the current data is obtained, and when one of the two occupies a large proportion, the one occupying a large proportion is re-collected as the closest value.

7. The power supply fault location system of distribution network line based on intelligent distributed ranging is applied to the power supply fault location method of distribution network line based on intelligent distributed ranging as claimed in claim 1, characterized in that, Comprise: The data acquisition module is used for obtaining a preset line historical fault positioning point, dividing the preset line into several line segments, and obtaining the line segment corresponding to each historical fault positioning point; and based on the obtained line segment corresponding to each historical fault positioning point, the historical fault occurrence frequency of each line segment is counted, and the fault coordinates corresponding to any one fault are obtained; The data division module is used for counting the number of fault coordinates, and dividing the divided line segment based on the counted number of fault coordinates; The distinguishing method comprises distinguishing the line section into a high failure rate line section, a medium failure rate line section and a low failure rate line section, collecting the current value of the preset line initial point when a failure occurs based on the high failure rate line section, collecting the current value from the current value to the line section corresponding to the failure coordinate based on the current value, and generating a current value coefficient A R ; The fusion analysis unit is used for collecting at least 10 current data according to the distance from the preset line initial point to the corresponding line segment R The fusion analysis unit is used for collecting at least 10 current data according to the distance from the preset line initial point to the corresponding line segment The fusion analysis unit is used for collecting at least 10 current data according to the distance from the preset line initial point to the corresponding line segment The determination module is used for when the change rule is that the current data increases, then the preset safety threshold is based on the current data with the highest occurrence frequency to the corresponding line segment, and when the current data of the preset line to the initial point of the corresponding line segment in the future period is consistent with the safety threshold, it is determined that the current of the line segment will increase, and a fault warning is issued, otherwise, it is not determined; The confidence calculation module is used for obtaining the confidence of the occurrence of fault of different line segments in the future period based on the first three current data in different current data collection numbers in response to the fusion analysis unit.

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