Power transmission line distributed fault diagnosis system and diagnosis method suitable for power internet of things

Abstract

The invention relates to a power transmission line distributed fault diagnosis system and diagnosis method suitable for an electric power internet of things. The system comprises a data center station and a monitoring terminal deployed on a power transmission line. The plurality of monitoring terminals are respectively in wireless communication connection with the data center station; through technology fusion and multi-dimensional data utilization, compared with the prior art, the accuracy, the real-time performance and the reliability of fault detection, the operation and maintenance efficiency and the cost effectiveness are remarkably improved. The fault diagnosis method comprises interval positioning, accurate positioning and result output. The monitoring terminal is used as a demarcation point to divide the line into a plurality of sections, and after a fault occurs, the pre-processing module can determine the section where the fault point is located according to the direction and size of fault current or traveling wave current and reduce the fault point finding range, so that the fault point finding efficiency is improved, and typical fault reasons of the power transmission line are identified according to the waveform of the traveling wave current. And the reason of the circuit fault can be quickly determined.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line fault diagnosis, and in particular to a power transmission line distributed fault diagnosis system and a diagnosis method suitable for an electric power Internet of Things. Background Art

[0002] The distributed fault diagnosis system for transmission lines of the power Internet of Things reduces power outage time and improves the reliability and power supply quality of the power grid by real-time monitoring and rapid fault diagnosis. After searching, the Chinese patent announcement number CN110749786A discloses a distributed fault diagnosis system for transmission lines suitable for the power Internet of Things. Although the system can reduce energy consumption, improve safety, reduce equipment size, expand the scope of application, and reduce operating costs, the fault diagnosis system can only detect the location of the fault and cannot accurately determine the cause of the fault based on the waveform signal fed back by the sensor. For this reason, the present invention proposes a distributed fault diagnosis system and diagnosis method for transmission lines suitable for the power Internet of Things, which can timely and accurately determine the specific location of the fault and the cause of the fault, thereby improving the speed and accuracy of fault diagnosis. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a distributed fault diagnosis system and a diagnosis method for power transmission lines suitable for the power Internet of Things, so as to overcome the deficiencies in the above-mentioned prior art.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: a distributed fault diagnosis system for power transmission lines applicable to the power Internet of Things, comprising a data center station and monitoring terminals deployed on the transmission lines; a plurality of the monitoring terminals are respectively connected to the data center station by wireless communication;

[0005] The monitoring terminal includes a data acquisition module, a data transmission module, a power supply module and a clock module:

[0006] The data acquisition module is used to collect multi-dimensional data including current, voltage, temperature, humidity, wind speed and wind direction in real time, and automatically identify traveling wave current and fault current, and collect and store waveform data of the traveling wave current and the fault current;

[0007] The clock module is used to mark the timestamp of data during data collection and transmission, so as to facilitate subsequent data analysis and fault diagnosis;

[0008] The data transmission module is used to send the waveform data of the traveling wave current and the fault current collected by the data collection module and the clock data marked by the clock module to the data center station in real time by wireless communication;

[0009] The power supply module is used to provide the required power to the monitoring terminal;

[0010] The data center station includes a pre-processing module, a fault diagnosis module and an application service module;

[0011] The preprocessor is used to preprocess the information collected by the monitoring terminal, analyze it after preprocessing, and determine the fault point according to the analysis result. The information of the fault point is transmitted to the fault diagnosis module through encryption;

[0012] The fault diagnosis module is used to detect faults, monitor the operating status of the power transmission line, analyze the data collected from the monitoring terminal in real time, and analyze the collected data to determine the fault type of the fault point;

[0013] The application service module is used to send the fault point and fault type analyzed by the data center station to the remote terminal platform.

[0014] The beneficial effects of the present invention are: the distributed fault diagnosis system for power transmission lines of the power Internet of Things of the present invention has significantly improved the accuracy, real-time performance, reliability, operation and maintenance efficiency and cost-effectiveness of fault detection compared with the existing technology through technology integration and multi-dimensional data utilization.

[0015] The present invention also discloses a distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things, which uses the above-mentioned distributed fault diagnosis system for power transmission lines for diagnosis, and includes the following steps:

[0016] Step S01: Interval positioning: using the monitoring terminal as the dividing point, the transmission line is divided into several intervals. After a fault occurs, the pre-processing module determines the interval where the fault point is located according to the direction and magnitude of the fault current or traveling wave current, thereby narrowing the search range of the fault point.

[0017] Step S02: accurate positioning, through the pre-processing module according to the traveling wave positioning method, determine the exact location of the fault point, the traveling wave positioning method includes the double-end positioning method and the single-end positioning method;

[0018] Step S03: Fault identification: storing the traveling wave current waveforms of different fault causes through the data center station, and establishing a fault traveling wave waveform database through the fault diagnosis module, and identifying the typical fault causes of the transmission line according to the identification standards of the traveling wave current waveforms of different fault causes;

[0019] Step S04: Result output: after a line fault occurs, the fault point and fault type are output to the remote terminal platform through the application service module in the form of text messages and WEB publishing.

[0020] The beneficial effect of the present invention is as follows: the present invention divides the line into several sections by using the monitoring terminal as the dividing point. After a fault occurs, the pre-processing module can determine the section where the fault point is located according to the direction and size of the fault current or the traveling wave current, narrow the fault point search range, thereby improving the efficiency of fault point troubleshooting, storing the traveling wave current waveforms of different fault causes through the data center station, and establishing a fault traveling wave waveform database through the fault diagnosis module. According to the traveling wave current waveform, the typical fault causes of the transmission line are identified, which is conducive to quickly determining the cause of the circuit fault.

[0021] Based on the above technical solution, the present invention can also be improved as follows.

[0022] Further, the dual-end positioning method in S02 includes:

[0023] Monitoring terminals are set on both sides of the fault line. The distance calculation formulas between the fault point and the two monitoring terminals are:

[0024] L M =(L+v·(t M -t N )) / 2,

[0025] L N =(Lv·(t M -t N )) / 2,

[0026] Among them, L M is the distance between the fault point and the monitoring terminal M, L N is the distance between the fault point and the monitoring terminal N, L is the length between the monitoring terminal M and the monitoring terminal N; v is the speed of the traveling wave propagating on the transmission line; t M is the time when the fault traveling wave first reaches the monitoring terminal M; t N It is the moment when the fault traveling wave first reaches the monitoring terminal N.

[0027] Further, the single-end positioning method in S02 includes:

[0028] A monitoring terminal and a substation are set up on one side of the fault line, and the calculation formula is:

[0029]

[0030] Where L is the distance between the fault point and the monitoring terminal; t2 is the time when the fault wave propagating directly from the fault point to the monitoring terminal reaches the monitoring terminal for the second time after being reflected by the monitoring terminal and the substation for the first time; t3 is the time when the fault wave reaches the monitoring terminal for the third time after being reflected by the substation and the fault point.

[0031] Further, the single-end positioning method in S02 includes:

[0032] There is a monitoring terminal on one side of the fault line and a substation on the other side. The calculation formula is:

[0033]

[0034] Where L is the distance between the fault point and the substation; t2 is the time when the fault traveling wave propagating from the fault point to the substation reaches the monitoring terminal for the second time after being reflected by the substation; t1 is the time when the fault traveling wave propagating directly from the fault point to the monitoring terminal reaches the monitoring terminal for the first time.

[0035] Furthermore, the fault causes in S03 include: lightning fault, tree obstacle fault, forest fire fault and wind deviation fault, and the lightning fault includes bypass fault and strike back fault.

[0036] Furthermore, the identification criteria of the traveling current waveforms of different fault causes in S03 include:

[0037] Identification criteria for lightning faults: The fault phase traveling wave current waveform of a counter-strike fault includes the reverse polarity pulse induced before the flashover moment and the forward traveling wave of the lightning current after the flashover moment, while the non-fault phase traveling wave current waveform only includes the induced current with the opposite polarity to the lightning current; the fault phase traveling wave current of a bypass fault is the lightning current flowing through the fault phase before the flashover and the reflected wave of the lightning current flowing through the tower at the fault point and into the ground after the flashover. The two have the same polarity and no reverse polarity pulse will appear after superposition;

[0038] Identification criteria for tree faults: The waveform characteristics are that the falling edge of the wave head is very slow, and the rising edge of the wave head is steeper than other high-resistance grounding faults; there is intermittent flashover before the main peak of the discharge; the amplitude of the traveling wave is less than 100A;

[0039] Identification criteria for wildfire faults: The waveform characteristics are that the rising and falling edges of the main wave are relatively gentle, and the half-peak time of the wave head is long; the amplitude of the traveling wave is less than 300A; the waveform is relatively smooth, there is no obvious pre-discharge feature on the rising edge of the main wave, and there is a weak pre-discharge before the main discharge;

[0040] Identification criteria for wind deviation faults: The waveform characteristics are that the main wave has a steeper rising edge, a short wave head time, and a long half-peak time; multiple faults often occur in a short period of time, and the main waves have a high degree of similarity due to the same discharge channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the module of the present invention;

[0042] Figure 2 It is a schematic diagram of the positioning model within the AC line section of the present invention;

[0043] Figure 3 A comparison diagram of the fault current waveform within the AC line section of the present invention;

[0044] Figure 4 This is a schematic diagram of the AC line section external positioning model of the present invention;

[0045] Figure 5 A comparison diagram of the fault current waveform outside the AC line section of the present invention;

[0046] Figure 6 This is a schematic diagram of a DC line section positioning model of the present invention;

[0047] Figure 7 It is a schematic diagram of the traveling wave current waveform at the monitoring terminal M in the DC line section of the present invention;

[0048] Figure 8 It is a schematic diagram of the waveform of the traveling wave current at the monitoring terminal N in the DC line section of the present invention;

[0049] Fig. 9 This is a schematic diagram of a DC line section positioning model of the present invention;

[0050] Fig.10 A schematic diagram of a traveling wave current waveform at an external measurement terminal M in a DC line section of the present invention;

[0051] Fig.11 It is a schematic diagram of the waveform of the traveling wave current at the monitoring terminal N outside the DC line section of the present invention;

[0052] Fig.12 It is a schematic diagram of the double-end traveling wave positioning model of the present invention;

[0053] Fig.13 This is a schematic diagram of single-ended fault location method I of the present invention;

[0054] Fig.14 This is the principle diagram of single-ended fault location method II of the present invention;

[0055] Fig.15 This is a schematic diagram of a typical traveling wave waveform of a shield failure of the present invention;

[0056] Fig.16 This is a schematic diagram of a typical traveling wave waveform of the present invention;

[0057] Fig.17 This is a typical waveform diagram of the tree barrier of the present invention;

[0058] Fig.18 This is a typical waveform diagram of a wildfire fault of the present invention;

[0059] Fig.19 This is a schematic diagram of a typical waveform of a wind deviation fault in the present invention. DETAILED DESCRIPTION

[0060] The principles and features of the present invention are described below in conjunction with 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.

[0061] like Figure 1 As shown, in embodiment 1, a distributed fault diagnosis system for power transmission lines applicable to the power Internet of Things includes a data center station and monitoring terminals deployed on the transmission lines; a plurality of monitoring terminals are respectively wirelessly connected to the data center station;

[0062] The monitoring terminal includes data acquisition module, data transmission module, power module and clock module:

[0063] The data acquisition module is used to collect multi-dimensional data including current, voltage, temperature, humidity, wind speed and wind direction in real time, and automatically identify traveling wave current and fault current, and collect and store the waveform data of traveling wave current and fault current; in specific implementation, the data acquisition module includes sensors and monitoring equipment deployed on the transmission line;

[0064] The clock module is used to mark the timestamp of data during data collection and transmission, which is convenient for subsequent data analysis and fault diagnosis. The clock module has a high-precision satellite synchronous clock function, and the calibration accuracy of the synchronous clock is no more than 0.1μ.

[0065] The data transmission module is used to send the waveform data of the traveling wave current and the fault current collected by the data collection module and the clock data marked by the clock module to the data center station in real time by wireless communication;

[0066] The power module is used to provide the required power to the monitoring terminal;

[0067] The data center station includes a pre-processing module, a fault diagnosis module, and an application service module;

[0068] The preprocessor is used to preprocess the information collected by the monitoring terminal, analyze it after preprocessing, and determine the fault point according to the analysis result. The information of the fault point is transmitted to the fault diagnosis module through encryption;

[0069] Fault diagnosis module, used to detect faults, monitor the operating status of the transmission line, analyze the data collected from the monitoring terminal in real time, and analyze the collected data to determine the fault type of the fault point;

[0070] The application service module is used to send the fault points and fault types analyzed by the data center station to the remote terminal platform.

[0071] The distributed fault diagnosis system for power transmission lines of the electric power Internet of Things of the present invention has significantly improved the accuracy, real-time performance, reliability, operation and maintenance efficiency, and cost-effectiveness of fault detection compared to the prior art through technology integration and multi-dimensional data utilization.

[0072] like Figures 2 to 19 As shown, Example 2, a distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things, adopts the distributed fault diagnosis system for power transmission lines in Example 1 for diagnosis, and includes the following steps:

[0073] Step S01: Interval positioning: using the monitoring terminal as the dividing point, the transmission line is divided into several intervals. After a fault occurs, the pre-processing module determines the interval where the fault point is located according to the direction and size of the fault current or traveling wave current, thereby narrowing the search range of the fault point.

[0074] Step S02: Accurate positioning: determine the exact location of the fault point through the pre-processing module according to the traveling wave positioning method. The traveling wave positioning method includes the double-end positioning method and the single-end positioning method. In the specific implementation, the double-end positioning is mainly used, and the single-end positioning is supplemented. It is suitable for overhead lines, overhead cable hybrid lines and overhead section fault positioning of T-connected lines.

[0075] Step S03: Fault identification: storing the traveling wave current waveforms of different fault causes through the data center station, and establishing a fault traveling wave waveform database through the fault diagnosis module, and identifying the typical fault causes of the transmission line according to the identification standards of the traveling wave current waveforms of different fault causes;

[0076] Step S04: Result output: after a line fault occurs, the fault point and fault type are output to the remote terminal platform through the application service module in the form of text messages and WEB publishing, and historical fault data can be queried through the application service module.

[0077] The present invention divides the line into several sections by using the monitoring terminal as a dividing point. After a fault occurs, the pre-processing module can determine the section where the fault point is located according to the direction and size of the fault current or the traveling wave current, narrow the fault point search range, and thus improve the efficiency of fault point troubleshooting. The traveling wave current waveforms of different fault causes are stored in the data center station, and a fault traveling wave waveform database is established through the fault diagnosis module. According to the traveling wave current waveform, the typical fault causes of the transmission line are identified, which is conducive to quickly determining the cause of the circuit fault.

[0078] Embodiment 3, this embodiment is a further improvement on the basis of embodiment 2, and its details are as follows:

[0079] The dual-end positioning method in S02 includes:

[0080] Monitoring terminals are set up on both sides of the fault line to monitor the initial traveling waves reaching the monitoring points where the two monitoring terminals are located, thus forming a double-terminal positioning. Fig.12 As shown in the figure, the fault line is between substation A and substation B. Points M and N are monitoring points equipped with monitoring terminals M and N, respectively. The fault occurs at point C between M and N. The initial traveling wave generated by the fault point C is transmitted along the transmission line to substation A and substation B at both ends at a speed v. The time when it arrives at both ends (end M and end N) is t M ,t N , the distance calculation formulas between the fault point and the two monitoring terminals are:

[0081] L M =(L+v·(t M -t N )) / 2,

[0082] L N =(Lv·(t M -t N )) / 2,

[0083] Among them, L M is the distance between the fault point and the monitoring terminal M, L N is the distance between the fault point and the monitoring terminal N, L is the length between the monitoring terminal M and the monitoring terminal N; v is the speed of the traveling wave propagating on the transmission line; t M is the time when the fault traveling wave first reaches the monitoring terminal M; t N It is the moment when the fault traveling wave first reaches the monitoring terminal N.

[0084] Embodiment 4: This embodiment is a further improvement on the basis of embodiment 2, and its details are as follows:

[0085] The single-ended positioning method in S02 includes:

[0086] like Fig.13 As shown in the schematic diagram of single-end fault location method I, a monitoring terminal M and a substation B are provided on one side of the fault point C. The calculation formula is:

[0087]

[0088] Where L is the distance between the fault point C and the monitoring terminal M; t2 is the time when the fault wave propagating directly from the fault point C to the monitoring terminal M reaches the monitoring terminal M for the second time after being reflected by the monitoring terminal M and the B substation for the first time; t3 is the time when the fault wave reaches the monitoring terminal M for the third time after being reflected by the B substation and the fault point. The monitoring terminal M records the time when the fault wave passes through the monitoring point, which can constitute the single-ended traveling wave fault location.

[0089] Embodiment 5: This embodiment is a further improvement on the basis of embodiment 2, and its details are as follows:

[0090] The single-ended positioning method in S02 includes:

[0091] like Fig.14 As shown in the schematic diagram of single-ended fault location method II, a monitoring terminal M is provided on one side of the fault point C and a substation A is provided on the other side. The calculation formula is:

[0092]

[0093] Where L is the distance between the fault point C and the A substation; t2 is the time when the fault traveling wave propagating from the fault point C to the A substation reaches the monitoring terminal M for the second time after being reflected by the A substation; t1 is the time when the fault traveling wave propagating directly from the fault point C to the monitoring terminal M first arrives at the monitoring terminal M. The monitoring terminal M records the time when the fault traveling wave passes through the monitoring point, which can constitute the single-ended traveling wave fault location.

[0094] Embodiment 6: This embodiment is a further improvement on the basis of embodiment 2, and its details are as follows:

[0095] The fault causes in S03 include: lightning fault, tree obstacle fault, forest fire fault and wind deviation fault. Lightning fault includes bypass fault and strike-back fault.

[0096] Embodiment 7, this embodiment is a further improvement on the basis of embodiment 6, and its details are as follows:

[0097] The identification criteria of the traveling current waveform of different fault causes in S03 include:

[0098] The fault traveling wave current flowing through the line due to lightning fault is mainly composed of two parts, including the lightning current directly entering the line after being shunted and the lightning current entering the line after being reflected by the tower into the ground; the two parts have opposite polarities, and the initial lightning current wave tail decays rapidly after the superposition of the two parts. The measured half-peak time of the lightning fault traveling wave is generally within 20μs. The typical waveform of the lightning fault is as follows Fig.15 (bypass fault), Fig.16 As shown (reverse fault); for tree barriers, wildfires and other grounding faults, the fault traveling wave current flowing through the line is a step response generated by the power frequency voltage at the moment of grounding. Its peak value decays slowly, the wave tail is long, and the measured half-peak time of the traveling wave is greater than 20μs;

[0099] Identification criteria for lightning faults: before the lightning strikes the top of the tower and causes the insulator string to flash over, the lightning current first flows through the lightning conductor, which will induce a pulse with the opposite polarity to the lightning current on each phase of the transmission line. After the flashover, the lightning current flows through the fault phase, and the non-fault phase continues to be induced by the lightning current. Therefore, the fault phase traveling wave current waveform of the counter-strike fault includes the reverse polarity pulse induced before the flashover moment and the forward traveling wave of the lightning current after the flashover moment, and the non-fault phase traveling wave current waveform only includes the induced current with the opposite polarity to the lightning current; the fault phase traveling wave current of the bypass fault is the lightning current flowing through the fault phase before the flashover and the reflected wave of the part of the lightning current flowing through the tower at the fault point and into the ground after the flashover. The two have the same polarity, and no reverse polarity pulse will appear after superposition;

[0100] Tree barrier fault is a discharge fault caused by tree branches touching the wire or less than the safe distance. The typical fault traveling wave waveform recorded by the monitoring terminal is as follows: Fig.17 As shown in the figure, the identification criteria of tree barrier faults are as follows: the waveform characteristics are that the falling edge of the wave head is very slow, and the rising edge of the wave head is steeper than other high-resistance grounding faults; there is intermittent flashover before the main peak of discharge; the amplitude of the traveling wave is less than 100A, and can be as low as the ampere level;

[0101] The discharge fault is caused by factors such as air heat ionization and smoke caused by wildfire. The typical fault traveling wave waveform recorded by the monitoring terminal is as follows: Fig.18 As shown in the figure, the identification criteria for wildfire faults are as follows: the waveform characteristics are that the rising and falling edges of the main wave are relatively gentle, and the half-peak time of the wave head is long; the amplitude of the traveling wave is less than 300A; the waveform is relatively smooth, there is no obvious pre-discharge feature on the rising edge of the main wave, and there is a weak pre-discharge before the main discharge;

[0102] Due to strong wind, the distance between the conductor and the tower or lightning conductor is less than the safe distance, resulting in a discharge fault. The typical fault waveform recorded by the monitoring terminal is as follows: Fig.19 As shown in the figure, the identification criteria for wind deviation faults are: the waveform characteristics are that the main wave has a steep rising edge, a short wave head time, and a long half-peak time; multiple faults often occur in a short period of time, and the main waves have a high similarity due to the same discharge channel.

[0103] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A distributed fault diagnosis system for power transmission lines suitable for power Internet of Things, characterized in that: It includes a data center station and monitoring terminals deployed on the transmission line; a plurality of the monitoring terminals are respectively connected to the data center station by wireless communication; The monitoring terminal includes a data acquisition module, a data transmission module, a power supply module and a clock module: The data acquisition module is used to collect multi-dimensional data including current, voltage, temperature, humidity, wind speed and wind direction in real time, and automatically identify traveling wave current and fault current, and collect and store waveform data of the traveling wave current and the fault current; The clock module is used to mark the timestamp of data during data collection and transmission, so as to facilitate subsequent data analysis and fault diagnosis; The data transmission module is used to send the waveform data of the traveling wave current and the fault current collected by the data collection module and the clock data marked by the clock module to the data center station in real time by wireless communication; The power supply module is used to provide the required power to the monitoring terminal; The data center station includes a pre-processing module, a fault diagnosis module and an application service module; The preprocessor is used to preprocess the information collected by the monitoring terminal, analyze it after preprocessing, and determine the fault point according to the analysis result. The information of the fault point is transmitted to the fault diagnosis module through encryption; The fault diagnosis module is used to detect faults, monitor the operating status of the power transmission line, analyze the data collected from the monitoring terminal in real time, and analyze the collected data to determine the fault type of the fault point; The application service module is used to send the fault point and fault type analyzed by the data center station to the remote terminal platform.

2. A distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things, characterized in that: The distributed fault diagnosis system for power transmission lines in claim 1 is used for diagnosis, comprising the following steps: Step S01: Interval positioning, using the monitoring terminal as a dividing point, dividing the transmission line into several intervals. After a fault occurs, the pre-processing module determines the interval where the fault point is located according to the direction and magnitude of the fault current or the traveling wave current, thereby narrowing the search range of the fault point; Step S02: accurate positioning, determining the exact location of the fault point through the pre-processing module according to the traveling wave positioning method, wherein the traveling wave positioning method includes a double-end positioning method and a single-end positioning method; Step S03: Fault identification, storing the traveling wave current waveforms of different fault causes through the data center station, and establishing a fault traveling wave waveform database through the fault diagnosis module, and identifying the typical fault causes of the transmission line according to the identification standards of the traveling wave current waveforms of different fault causes; Step S04: Result output: after a line fault occurs, the fault point and fault type are output to the remote terminal platform through the application service module in the form of mobile phone text messages and WEB publishing.

3. A distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things according to claim 2, characterized in that: The dual-end positioning method in S02 includes: The monitoring terminals are respectively arranged on both sides of the fault line, and the distance calculation formulas between the fault point and the two monitoring terminals are respectively: Among them, L M is the distance between the fault point and the monitoring terminal M, L N is the distance between the fault point and the monitoring terminal N, L is the length between the monitoring terminal M and the monitoring terminal N; v is the speed of the traveling wave propagating on the transmission line; t M is the time when the fault traveling wave first reaches the monitoring terminal M; t N It is the moment when the fault traveling wave first reaches the monitoring terminal N.

4. A distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things according to claim 2, characterized in that: The single-end positioning method in S02 includes: The monitoring terminal and substation are provided on one side of the fault line, and the calculation formula is: Where L is the distance between the fault point and the monitoring terminal; t2 is the time when the fault wave propagating directly from the fault point to the monitoring terminal reaches the monitoring terminal for the second time after being reflected by the monitoring terminal and the substation for the first time; t3 is the time when the fault wave reaches the monitoring terminal for the third time after being reflected by the substation and the fault point.

5. A distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things according to claim 2, characterized in that: The single-end positioning method in S02 includes: The monitoring terminal is provided on one side of the fault line and a substation is provided on the other side. The calculation formula is: Where L is the distance between the fault point and the substation; t2 is the time when the fault traveling wave propagating from the fault point to the substation reaches the monitoring terminal for the second time after being reflected by the substation; t1 is the time when the fault traveling wave propagating directly from the fault point to the monitoring terminal reaches the monitoring terminal for the first time.

6. A distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things according to claim 2, characterized in that: The fault causes in S03 include: lightning strike fault, tree obstacle fault, mountain fire fault and wind deviation fault, and the lightning strike fault includes shielding fault and counter-strike fault.

7. A distributed fault diagnosis method for power transmission lines applicable to the power Internet of Things according to claim 6, characterized in that: The identification criteria of the traveling current waveforms of the different fault causes in S03 include: The identification standard of the lightning fault is as follows: the fault phase traveling wave current waveform of the counter-strike fault includes the reverse polarity pulse induced before the flashover moment and the forward traveling wave of the lightning current after the flashover moment, and the non-fault phase traveling wave current waveform only includes the induced current with the opposite polarity to the lightning current; the fault phase traveling wave current of the bypass fault fault is the lightning current flowing through the fault phase before the flashover and the reflected wave of the part of the lightning current flowing through the tower at the fault point and into the ground after the flashover, and the two have the same polarity, and no reverse polarity pulse will appear after superposition; The identification criteria of the tree barrier fault are as follows: the waveform characteristics are that the falling edge of the wave head is very slow, and the rising edge of the wave head is steeper than other high-resistance grounding faults; there is intermittent flashover before the main peak of the discharge; the amplitude of the traveling wave is less than 100A; The identification criteria of the mountain fire fault are as follows: the waveform characteristics are that the rising and falling edges of the main wave are relatively gentle, and the half-peak time of the wave head is long; the amplitude of the traveling wave is less than 300A; the waveform is relatively smooth, there is no obvious pre-discharge feature on the rising edge of the main wave, and there is a weak pre-discharge before the main discharge; The identification criteria of the wind deviation fault are as follows: the waveform characteristics are that the main wave has a steep rising edge, a short wave head time, and a long half-peak time; multiple faults often occur in a short period of time, and the main waves have a high similarity due to the same discharge channel.

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