CN111880046A - A device and method for quickly identifying the cause of a line fault - Google Patents

Abstract

The invention discloses a device and method for quickly identifying the cause of a line fault, and relates to the technical field of power line monitoring; the device includes a module for acquiring fault recording information, a module for establishing a fault feature model, and a module for intelligent analysis; the method includes the following steps. , the fault recorder system obtains the fault recorder information at the time of the fault and sends it to the server, where the fault recorder information includes fault voltage information and fault current information; the second step, the server receives the fault recorder system from the fault recorder at the time of the fault. Wave recorder information, extract the fault feature quantity from the fault recorder information, establish the mapping relationship between the fault feature quantity and the fault cause, and form the fault feature model; the third step, the server inputs the acquired fault recorder information into the fault feature model And obtain the corresponding fault cause; it can quickly identify the line fault cause with high accuracy by acquiring the fault recording information module, establishing the fault feature model module and the intelligent analysis module.

Description

A device and method for quickly identifying the cause of a line fault

technical field

The invention relates to the technical field of power line monitoring, in particular to a device and method for quickly identifying the cause of a line fault.

Background technique

As shown in Figure 5, at present, power grid dispatchers mainly rely on the voltage, current, switch displacement, protection action and other information provided by the fault recording system to judge the fault phase and type. However, due to the limitation of functions, the fault recording system cannot quickly provide the specific reasons for the line disconnection. It can only use the ranging information provided by the fault recording system to determine the reason for the disconnection after on-site inspection by the line patrol personnel, which takes a long time.

The main function of the fault recording system is to provide information on electrical quantities such as voltage and current at the time of the fault, as well as information on changes in switch displacement and protection actions. Therefore, although the lines with different fault causes (lightning strikes, foreign objects overlap, etc.) have different characteristics in the voltage, current and other information at the time of the fault, due to the lack of functions, the fault recording system alone cannot directly and quickly push the cause of the trip. .

As mentioned above, the on-site line inspection with the help of ranging information, although the reason for the line disconnection can be obtained, but it needs to rely on labor, lack of technical means, time-consuming and low efficiency, does not meet the demand for rapid restoration of power transmission, and ensures the safe operation of large power grids bring danger.

In order to ensure the safety of the power grid, according to the provisions of the power grid operation regulations, line failures caused by lightning strikes and other severe weather can be quickly tested. If technical means can be used to quickly identify the cause of the fault after a line fault, it is extremely important to quickly restore the line to operation and ensure the safety and stability of the power grid.

Existing technical problems and thinking:

How to solve the technical problems of slow speed and low efficiency in identifying the cause of line faults.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide a device and method for quickly identifying the cause of a line fault, which can quickly identify the cause of a line fault with high accuracy by acquiring a fault recording information module, establishing a fault feature model module and an intelligent analysis module, etc. .

In order to solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a device for quickly identifying the cause of a line fault includes three program modules: a module for acquiring fault recording information, a module for establishing a fault feature model, and a module for intelligent analysis. The wave information module is used to obtain the fault wave recorder information at the time of the fault and use it for the establishment of the fault feature model module. The fault characteristic quantity is extracted from the information, the mapping relationship between the fault characteristic quantity and the fault cause is established, and the fault characteristic model is formed; the intelligent analysis module is used to input the acquired fault recording information into the fault characteristic model and obtain the corresponding fault cause.

A further technical solution is as follows: it also includes a fault recording system and a server, wherein the fault recording system is connected to the server and communicates unidirectionally to obtain the fault recording information module, and is also used for the fault recording system to obtain the fault recording at the time of the fault. information and send it to the server; establish a fault feature model module, which is also used by the server to receive the fault recorder information at the time of the fault sent by the fault recorder system, and extract the feature quantities closely related to the fault occurrence from the fault recorder information; The analysis module is also used for the server to obtain the fault recorder information, input the fault feature model and find the fault cause.

A further technical solution is as follows: a module for obtaining lightning location information is further included, which is used for obtaining lightning information and is used by a module for establishing a fault feature model, wherein the lightning information includes lightning location information and lightning current; The fault feature quantity is extracted from the fault recording information and lightning location information, the mapping relationship between the fault feature quantity and the fault cause is established, and the fault feature model is formed; the intelligent analysis module is also used to obtain the obtained fault recording information and lightning information. Enter the fault feature model and obtain the corresponding fault cause.

A further technical solution is as follows: it also includes a lightning positioning system and a module for obtaining lightning positioning information, the lightning positioning system is connected with the server and communicates unidirectionally, and a module for obtaining lightning positioning information is used for the lightning positioning system to obtain lightning falling information and send it to the server, The lightning location information is the location of the lightning strike; a fault feature model module is established, which is also used for the server to receive the lightning strike information sent by the lightning location system, and extract the feature quantity closely related to the occurrence of the fault from the fault recording information and the lightning location information; The intelligent analysis module is also used for the server to obtain the fault recording information and lightning strike information, input the fault feature model and find the fault cause.

A further technical solution is: in establishing the fault characteristic model module, the fault characteristic quantity includes the harmonic component in the fault voltage, the DC component in the fault current and the lightning strike location; the data of the fault characteristic quantity and the fault cause are saved and formed. Fault cause information base.

A further technical solution is: in the establishment of the fault feature model module, the fault zero-sequence current waveform, the fault phase voltage, the phase current DC content and harmonic components and the reclosing information are extracted from the fault recording information as the fault numerical characteristics. The lightning location information is extracted from the information as the numerical feature of the fault, and the BP neural network is used to establish the mapping relationship between the fault feature quantity and the fault cause. The feature model is the fault feature BP network model.

A device for quickly identifying the cause of a line fault includes a memory and a processor, and also includes a module for acquiring fault recording information, a module for acquiring lightning location information, a module for establishing a fault feature model, and a module for intelligent analysis, which are stored in the memory and run on the processor. The module consists of four program modules.

A device for quickly identifying the cause of a line fault is a computer-readable storage medium, and the computer-readable storage medium stores a total of four modules: a module for acquiring fault recording information, a module for acquiring lightning location information, a module for establishing a fault feature model, and a module for intelligent analysis. program module.

A method for quickly identifying the cause of a line fault, based on a fault recording system and a server, wherein the fault recording system is connected to the server and communicates in one direction, and further comprises the following steps. In the first step, the fault recording system obtains the fault at the time of the fault. The wave recorder information is sent to the server, and the fault recorder information includes fault voltage information and fault current information; in the second step, the server receives the fault recorder information at the time of the fault sent by the fault recorder system, and obtains the fault recorder information from the fault recorder information. The fault characteristic quantity is extracted, the mapping relationship between the fault characteristic quantity and the fault cause is established, and the fault characteristic model is formed; in the third step, the server inputs the acquired fault recording information into the fault characteristic model and obtains the corresponding fault cause.

A further technical solution is: also based on a lightning location system, the lightning location system is connected to the server and communicates in one direction, in the first step, the lightning location system acquires lightning strike information and sends it to the server, where the lightning strike information includes lightning location information. and lightning current; in the second step, the server receives the lightning information sent by the lightning location system, extracts the fault feature quantity from the fault recording information and the lightning location information, establishes the mapping relationship between the fault feature quantity and the fault cause, and The fault feature model is formed; that is, the fault zero-sequence current waveform, fault phase voltage, phase current DC content and harmonic component and reclosing information are extracted from the fault recording information as the fault numerical characteristics, and the lightning location information is extracted from the lightning information as the fault. Numerical features, use BP neural network to establish the mapping relationship between the fault feature quantity and the fault cause, take the fault recording information and lightning information as the input vector, and take the fault cause as the output vector, and the established fault feature model is the fault feature BP network model. ; In the third step, the server inputs the acquired fault recording information and lightning strike information into the fault characteristic model and obtains the corresponding fault cause.

The beneficial effects produced by the above technical solutions are:

A device for quickly identifying the cause of a line fault includes three program modules: a module for acquiring fault recording information, a module for establishing a fault feature model, and a module for intelligent analysis; It is used for establishing a fault feature model module, and the fault recorder information includes fault voltage information and fault current information; a fault feature model module is established, which is used to extract the fault feature quantity from the fault recorder information, and establish the fault feature quantity and the fault cause. The mapping relationship between them forms a fault characteristic model; the intelligent analysis module is used to input the acquired fault recording information into the fault characteristic model and obtain the corresponding fault cause. By acquiring the fault recording information module, establishing the fault feature model module and intelligent analysis module, etc., it can quickly identify the cause of the line fault with high accuracy.

A device for quickly identifying the cause of a line fault includes a memory and a processor, and also includes a module for acquiring fault recording information, a module for acquiring lightning location information, a module for establishing a fault feature model, and a module for intelligent analysis, which are stored in the memory and run on the processor. The module consists of four program modules. By acquiring the fault recording information module, establishing the fault feature model module and intelligent analysis module, etc., it can quickly identify the cause of the line fault with high accuracy.

A device for quickly identifying the cause of a line fault is a computer-readable storage medium, and the computer-readable storage medium stores a total of four modules: a module for acquiring fault recording information, a module for acquiring lightning location information, a module for establishing a fault feature model, and a module for intelligent analysis. program module. By acquiring the fault recording information module, establishing the fault feature model module and intelligent analysis module, etc., it can quickly identify the cause of the line fault with high accuracy.

A method for quickly identifying the cause of a line fault, based on a fault recording system and a server, wherein the fault recording system is connected to the server and communicates in one direction, and further comprises the following steps. In the first step, the fault recording system obtains the fault at the time of the fault. The wave recorder information is sent to the server, and the fault recorder information includes fault voltage information and fault current information; in the second step, the server receives the fault recorder information at the time of the fault sent by the fault recorder system, and obtains the fault recorder information from the fault recorder information. The fault characteristic quantity is extracted, the mapping relationship between the fault characteristic quantity and the fault cause is established, and the fault characteristic model is formed; in the third step, the server inputs the acquired fault recording information into the fault characteristic model and obtains the corresponding fault cause. Through the above steps, etc., it can quickly identify the cause of the line fault with high accuracy.

For details, please refer to the description in the detailed description.

Description of drawings

Fig. 1 is the principle block diagram of Embodiment 1 of the present invention;

Fig. 2 is the principle block diagram of Embodiment 2 of the present invention;

3 is a flowchart of Embodiment 3 of the present invention;

4 is a flowchart of Embodiment 4 of the present invention;

FIG. 5 is a schematic block diagram of the prior art.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can do so without departing from the connotation of the present application Similar promotion, therefore, the present application is not limited by the specific embodiments disclosed below.

Example 1:

As shown in FIG. 1 , the present invention discloses a device for quickly identifying the cause of a line fault, including a fault recording system and a server for acquiring fault recording information, a module 101 for acquiring fault recording information, a module 102 for establishing a fault feature model, and a The intelligent analysis module 103 consists of three program modules, and the fault recording system is connected to the server and communicates in one direction.

A fault wave recording information acquisition module 101 is used for the fault wave recording system to obtain the fault wave record information at the time of the fault and send it to the server, where the fault wave record information includes fault voltage information, fault current information and reclosing information.

A fault feature model module 102 is established for the server to receive the fault recorder information at the time of the fault sent by the fault recorder system, extract the fault feature quantity from the fault recorder information, and establish a mapping relationship between the fault feature quantity and the fault cause And form the fault characteristic model. That is, the fault zero-sequence current waveform, fault phase voltage, phase current DC content, harmonic component and reclosing information are extracted from the fault recording information as the fault numerical characteristics, and the BP neural network is used to establish the mapping between the fault characteristic quantity and the fault cause. Taking the fault recording information as the input vector and the fault cause as the output vector, the established fault characteristic model is the fault characteristic BP network model.

The intelligent analysis module 103 is used for the server to obtain the fault recording information, input the fault feature model, and obtain the corresponding fault cause.

Among them, the fault recording system, the server itself, and the corresponding communication connection technology are the prior art and will not be repeated here.

Example 1 description:

The server uses the historical fault recording information of the fault recording system to establish and train the fault feature model. When a fault occurs at the monitored site, the fault recorder system acquires the fault recorder information that has just occurred and sends it to the server. The server inputs the newly occurred fault recorder information into the trained fault feature model and obtains the corresponding fault cause. It saves time and effort, and can quickly identify the cause of line faults with high accuracy. Identifiable failure causes include bird droppings failure, etc.

Example 2:

As shown in FIG. 2 , the present invention discloses a device for quickly identifying the cause of a line fault, including a fault recording system for acquiring fault recording information, a lightning location system and a server, and a module 201 for acquiring fault recording information, obtaining lightning location The information module 202 , the building fault feature model module 203 and the intelligent analysis module 204 have a total of four program modules. The fault recording system is connected to the server and communicates unidirectionally, and the lightning location system is connected to the server and communicates unidirectionally.

A fault wave recording information acquisition module 201 is used for the fault wave recording system to obtain the fault wave record information at the time of the fault and send it to the server, where the fault wave record information includes fault voltage information, fault current information and reclosing information.

The lightning location information acquisition module 202 is used for the lightning location system to acquire and send the lightning information to the server, where the lightning information includes lightning location information and lightning current, and the lightning location information is the location of the lightning.

A fault feature model module 203 is established for the server to receive the fault recorder information at the fault time sent by the fault recorder system and the lightning strike information sent by the lightning location system, and extract the fault feature quantity from the fault recorder information and the lightning location information , establish the mapping relationship between the fault characteristic quantity and the fault cause and form the fault characteristic model. That is, the fault zero-sequence current waveform, fault phase voltage, phase current DC content, harmonic component and reclosing information are extracted from the fault recording information as the fault numerical feature, and the lightning location information is extracted from the lightning information as the fault numerical feature. The neural network establishes the mapping relationship between the fault characteristic quantity and the fault cause, takes the fault recording information and lightning information as the input vector, and the fault cause as the output vector, and the established fault characteristic model is the fault characteristic BP network model.

The intelligent analysis module 204 is used for the server to obtain the fault recording information and the lightning strike information, input the fault characteristic model, and obtain the corresponding fault cause.

Among them, the fault recording system, the lightning location system, the server itself, and the corresponding communication connection technology are the prior art and will not be repeated here.

Description of Example 2:

The server uses the historical fault recording information of the fault recording system and the historical lightning information of the lightning location system to establish and train the fault feature model. When a fault occurs at the monitored site, the fault recording system obtains the just occurred fault recording information and sends it to the server, the lightning location system obtains the just occurred lightning information and sends it to the server, and the server records the newly occurred fault recording information and lightning The information is input into the trained fault feature model and the corresponding fault causes are obtained. The working process of this identification saves time and effort, and can quickly identify the cause of line faults with high accuracy. Identifiable failure causes further include causes of lightning failures.

Example 3:

As shown in FIG. 3 , the present invention discloses a method for quickly identifying the cause of a line fault. Based on the hardware device of Embodiment 1, the method includes the following steps:

S101 Acquire fault recording information

The fault recorder system acquires the fault recorder information at the time of the fault and sends it to the server, where the fault recorder information includes fault voltage information and fault current information.

S102 Establish a fault characteristic model

The server receives the fault recorder information at the time of the fault sent by the fault recorder system, extracts the fault feature quantity from the fault recorder information, establishes the mapping relationship between the fault feature quantity and the fault cause, and forms the fault feature model.

That is to extract the fault zero-sequence current waveform, fault phase voltage, phase current DC content and harmonic content and reclosing information from the fault recording information as the fault numerical characteristics, and use the BP neural network to establish the mapping between the fault feature quantity and the fault cause. Taking the fault recording information as the input vector and the fault cause as the output vector, the established fault characteristic model is the fault characteristic BP network model.

S103 Smart Analysis

The server inputs the acquired fault recorder information into the fault feature model and obtains the corresponding fault cause.

Example 4:

As shown in FIG. 4 , the present invention discloses a method for quickly identifying the cause of a line fault. Based on the hardware device of Embodiment 2, the method includes the following steps:

S201 obtains fault recording information and lightning location information

The lightning location system obtains and sends the information of lightning strikes to the server, where the lightning strikes information includes lightning location information and lightning current; the fault recording system obtains the fault recording information at the time of the fault and sends it to the server, and the fault recording information includes fault voltage information and fault current information. Among them, the lightning location system obtains the information of the falling lightning and the fault recorder system obtains the fault recorder information at the time of the fault, which are independent of each other, and there is no fixed sequence.

S202 Establish a fault characteristic model

The server receives the fault recorder information at the time of the fault sent by the fault recorder system and the lightning strike information sent by the lightning location system, extracts the fault feature quantity from the fault recorder information and the lightning location information, and establishes the relationship between the fault feature quantity and the fault cause. The mapping relationship between them and the fault feature model are formed.

That is, the fault zero-sequence current waveform, fault phase voltage, phase current DC content, harmonic component, and reclosing information are extracted from the fault recording information as the fault numerical feature, and the lightning location information is extracted from the lightning drop information as the fault numerical feature. The neural network establishes the mapping relationship between the fault characteristic quantity and the fault cause, takes the fault recording information and lightning information as the input vector, and the fault cause as the output vector, and the established fault characteristic model is the fault characteristic BP network model.

S203 Smart Analysis

The server inputs the acquired fault recording information and lightning strike information into the fault feature model and obtains the corresponding fault cause.

Example 5:

The invention discloses a device for quickly identifying the cause of a line fault, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implementing the steps of Embodiment 3 when the processor executes the computer program.

Example 6:

The present invention discloses a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in Embodiment 4 are implemented.

The inventive concept of this application:

Based on the fault recorder system and server, by acquiring the fault recorder information module, establishing the fault feature model module and the intelligent analysis module, perform the following steps to obtain the fault recorder information. The fault recorder system obtains the fault recorder information at the time of the fault and sends it to the server; establish a fault feature model, the server receives the fault recorder information at the time of the fault sent by the fault recorder system, extracts the fault feature quantity from the fault recorder information, establishes the mapping relationship between the fault feature quantity and the fault cause, and The fault feature model is formed; intelligent analysis, the server inputs the acquired fault recording information into the fault feature model and obtains the corresponding fault cause; realizes the rapid identification of the line fault cause with high accuracy.

Based on the fault recording system, the lightning location system and the server, through the acquisition of the fault recording information module, the acquisition of the lightning location information module, the establishment of the fault feature model module and the intelligent analysis module, the following steps are performed to obtain the fault recording information, and the fault recording system Obtain the fault recording information at the fault time and send it to the server; obtain the lightning location information, and the lightning location system obtains the lightning information and send it to the server; establish a fault feature model, and the server receives the fault recording information from the fault recording system at the fault time. As well as the lightning information sent by the lightning location system, the fault feature quantity is extracted from the fault recording information and the lightning location information, the mapping relationship between the fault feature quantity and the fault cause is established, and the fault feature model is formed; intelligent analysis, the server will obtain The fault recording information and lightning strike information are input into the fault feature model and the corresponding fault causes are obtained; the rapid identification of line fault causes is further realized with high accuracy.

Technical solution description:

As shown in Figure 2, the technical solution of the present application adds four new program modules on the basis of the existing technical solution (fault recording system): a module for obtaining fault recording information, a module for obtaining lightning location information, a module for establishing a fault feature model, and a module for obtaining fault recording information. Smart Analysis Module.

1. Obtain the fault recorder information module

Extract the voltage, current information and lightning location information at the fault moment in the fault recording system.

Line fault feature extraction:

After the line is faulty, the voltage and current information at the fault time in the fault recording system and the lightning related information in the lightning location system will be automatically pushed to the intelligent analysis module. The characteristic quantities closely related to the occurrence of the fault are extracted from the fault recording information and the lightning location information, such as the harmonic components in the fault voltage, the DC component in the fault current, and the location of the lightning strike.

The numerical properties of transmission line faults during flashover can be divided into two categories. One is the fault information directly observed from the oscillogram, including: the amplitude and phase of the voltage and current of each phase after the flashover, the change of the fault waveform, the reclosing situation after the fault tripping, etc. The other type is the numerical characteristics calculated and deduced from the fault recording data, such as fault phase voltage, phase current DC content and harmonic components. For the fault waveform whose waveform is non-standard sinusoidal, the Fourier series expansion method can be used. It is decomposed into a series of sums of sinusoids whose frequencies are positive integer multiples of the power frequency. In this way, the characteristic analysis and extraction of the DC content and harmonic components of the fault waveform are realized.

2. Obtain the lightning location information module

This module can obtain the lightning information provided by the lightning location system: lightning location (detailed to line towers), lightning current, etc.

3. Establish a fault feature model module

Based on the in-depth analysis of the fault mechanism and a large number of actual fault data, the characteristic quantities closely related to the occurrence of the fault are extracted from the fault recording information and lightning location information, and the effective connection between these fault characteristic quantities and the fault cause is established as the identification basis. .

Create a fault cause information base:

Based on the in-depth analysis of the fault mechanism and a large amount of actual fault data, the typical characteristic quantities of faults with different causes are summarized, and each characteristic information is stored in the fault cause information database.

4. Smart Analysis Module

When identifying the cause of a specific fault, it is only necessary to extract the relevant characteristics of the fault and refer to the known fault cause identification basis, and then a fault cause with the strongest corresponding relationship can be inferred and automatically pushed.

Fault cause identification:

According to the principle that the fault characteristic quantity must be closely related to the type of fault cause and can be effectively extracted and calculated, the fault zero-sequence current waveform, fault phase voltage, phase current DC content and harmonic content and reclosing information are extracted from the fault recording information as Effective fault numerical characteristics and supplemented by lightning location information. The advantage of nonlinear mapping between input and output can be established by using BP neural network. The fault recording information and lightning information are used as input vectors, and the fault causes are used as output vectors to establish multiple fault characteristic BP network models. The BP network algorithm trained by the sample can be used for fault cause identification.

For example, there are three characteristics of lightning strike failure:

(1) Voltage burr: The smart module decomposes the waveform to analyze whether there are harmonic components;

(2) The current sine wave contains almost no high-frequency harmonics: the intelligent module decomposes the waveform and draws the conclusion that there is only a sine wave;

(3) The fault impulse current is very large at the time of the fault, so there is a rich attenuated DC component: the smart module captures and extracts the DC component. Compared with the existing information database, it is concluded that the greater probability is lightning strike failure.

Summary of line fault characteristics:

1. Electrical characteristics when lightning strikes the gate:

(1) In the early stage of flashover, the lightning current wave head decays rapidly, resulting in large fluctuations in the voltage of the faulty phase, and burr-type disturbance in the initial voltage waveform.

(2) Lightning makes the wire and the ground (ground wire or tower) have insulation breakdown flashover, and then the power frequency voltage will continue to discharge along this flashover channel, developing into a power frequency arc, and the arc resistance and the tower resistance have a linear volt-ampere characteristic, the fault phase voltage waveform gradually becomes a neat and stable sine wave.

(3) Due to the intrusion of lightning current, there will be a large short-circuit impulse current in the line at the moment of the fault, so the fault phase current and zero-sequence current after the flashover contain a large attenuated DC component, and the fault current waveform is mostly Apparently asymmetrical sine wave.

(4) Lightning strike faults can generally be reclosed successfully.

2. Electrical characteristics of bird damage when the gate is off:

(1) The nature of fault grounding

The bird damage fault flashover channel is air arc and bird droppings with high electrical conductivity, which are similar to metallic faults.

(2) Characteristics of fault recording

The grounding resistance has linear volt-ampere characteristics, and the fault current waveform is mostly sine wave, which hardly contains attenuated DC components and high-frequency harmonic components. Statistical analysis of the phase voltage waveform of bird damage fault shows that the phase voltage waveform of bird damage fault is mostly sine wave (obviously different from the fault of crane hitting the line).

(3) The probability of successful reclosing of bird damage faults is relatively high.

3. Electric quantity characteristics when the crane touches the line and drops the brake:

(1) The nature of fault grounding

The fault of the crane touching the line is caused by the air breakdown caused by the metal car body approaching the transmission line, and the line is grounded with the car body along the air arc channel. The crane arm is generally a metal conductor with small and fixed resistance, and the arc for stable combustion is linear and low resistance.

(2) Characteristics of fault recording

Since the volt-ampere characteristic of the transition resistance is linear, the fault waveform is sinusoidal and contains less attenuated DC components and high-frequency harmonic components. Looking at the phase voltage waveform of the crane touching the line fault, it is found that the phase voltage waveform of the crane touching the line fault is mostly irregular sine wave.

(3) After the crane touches the line and causes the transmission line to trip, the boom generally cannot be moved quickly. During reclosing, because the short-circuit channel still exists, the line may trip again, so the fault reclosing is often unsuccessful.

4. Electrical characteristics when the foreign body is lapped and disconnected:

(1) The nature of fault grounding

The non-metallic foreign object wiring fault is caused by the line-to-object discharge caused by objects such as trees and kites approaching the wire. During the flashover process, the high temperature generated by the short-circuit current may cause the object to burn, resulting in a nonlinear random variation of the flashover channel. Therefore, the resistance value of the transition resistance of the non-metallic foreign body wiring fault is larger than that of the metallic resistance, and the volt-ampere characteristic of the transition resistance has a certain nonlinear characteristic.

(2) Characteristics of fault recording

For non-metallic foreign body wiring faults, due to the large and nonlinear resistance of the transition resistance, the fault phase voltage changes little after flashover, and the fault phase current and zero-sequence current waveforms are distorted and contain high-frequency harmonic components.

The wiring of foreign objects causes the transmission line to trip. Generally, the high temperature generated by the short-circuit current can fuse or burn the part of the wiring object in contact with the discharge channel, thereby increasing the air distance between the wire and the object. So the reclosing is easy to succeed.

Table 1: Feature library of electrical quantities for faults with different causes

After the application has been run confidentially for a period of time, the benefits of feedback from on-site technicians are:

After a line fault, the cause of the fault can be pushed along with the fault phase and type. After grasping the reason for the line failure and gate failure, it is possible to eliminate the situation of failure to restore power transmission such as tower collapse, disconnection, permanent grounding, etc., and provide strong support for the dispatcher to quickly test power transmission, especially in bad weather. The safety guarantee of transmission and power grid grids is of great significance. It can effectively shorten the risk time of full shutdown of 220kV and above substations and large-scale power outages of main grid grids, and provide a strong guarantee for social industry, residents' life continuity, and high-quality electricity consumption. .

At the same time, it can quickly determine the cause of the fault, which saves time for line patrol personnel to find fault points, secondary determination of fault causes, and defect elimination, and greatly improves work efficiency.

Claims (10)

1. a device for quickly identifying the cause of line fault, it is characterized in that: comprise three program modules for obtaining fault recording information module, setting up fault feature model module and intelligence analysis module, obtaining fault recording information module, for obtaining fault The fault recorder information at time is used for establishing a fault feature model module, the fault recorder information includes fault voltage information and fault current information; a fault feature model module is established to extract the fault feature quantity from the fault recorder information, The mapping relationship between the fault feature quantity and the fault cause is established, and the fault feature model is formed; the intelligent analysis module is used to input the acquired fault recording information into the fault feature model and obtain the corresponding fault cause. 2. The device for quickly identifying the cause of a line fault according to claim 1, further comprising a fault recording system and a server, wherein the fault recording system is connected with the server and communicates unidirectionally to obtain the fault recording The information module is also used for the fault recorder system to obtain the fault recorder information at the time of the fault and send it to the server; the establishment of the fault feature model module is also used for the server to receive the fault recorder information at the fault time sent by the fault recorder system, from the The feature quantity closely related to the occurrence of the fault is extracted from the fault recording information; the intelligent analysis module is also used for the server to obtain the fault recording information, input the fault feature model and find the fault cause. 3. The device for quickly identifying the cause of a line fault according to claim 1, further comprising a module for obtaining lightning location information, for obtaining lightning information and for use by a module for establishing a fault feature model, wherein the lightning information includes: Lightning locating information and lightning current; establishing a fault feature model module, which is also used to extract fault feature quantities from fault recorder information and lightning locating information, establish a mapping relationship between fault feature quantities and fault causes, and form a fault feature model; The intelligent analysis module is also used to input the acquired fault recording information and lightning strike information into the fault feature model and obtain the corresponding fault cause. 4. A device for quickly identifying the cause of a line fault according to claim 2, characterized in that: it also includes a lightning location system and a module for obtaining lightning location information, the lightning location system is connected to a server and communicates in one direction to obtain lightning The positioning information module is used for the lightning positioning system to obtain the lightning information and send it to the server. The lightning positioning information is the lightning location; the fault feature model module is established, and the server receives the lightning information sent by the lightning positioning system, and records the waves from the fault. The feature quantity closely related to the fault is extracted from the information and lightning location information; the intelligent analysis module is also used for the server to obtain the fault recording information and lightning information, input the fault feature model and find the fault cause. 5 . The device for quickly identifying the cause of a line fault according to claim 3 , wherein in the module for establishing a fault characteristic model, the fault characteristic quantities include harmonic components in the fault voltage and direct current in the fault current. 6 . Component and lightning location; save the data of fault characteristic quantity and fault cause and form a fault cause information base. 6. A device for quickly identifying the cause of a line fault according to claim 3, characterized in that: in establishing a fault feature model module, the fault zero-sequence current waveform, fault phase voltage, and phase current are extracted from the fault recording information. The DC content, harmonic component and reclosing information are taken as the numerical features of the fault, and the lightning location information is extracted from the lightning information as the numerical features of the fault, and the BP neural network is used to establish the mapping relationship between the fault feature quantity and the fault cause, and the fault recorder information and lightning information as the input vector, the fault cause as the output vector, the established fault characteristic model is the fault characteristic BP network model. 7. A device for quickly identifying the cause of a line fault, comprising a memory and a processor, and is characterized in that: also include an acquisition fault recording information module, an acquisition lightning location information module, an acquisition of a lightning location information module, which are stored in the memory and can run on the processor. There are four program modules in total, fault feature model module and intelligent analysis module. 8. A device for quickly identifying the cause of a line fault, which is a computer-readable storage medium, characterized in that: the computer-readable storage medium stores a module for obtaining fault recording information, a module for obtaining lightning location information, and a module for establishing a fault feature model. There are four program modules in total with the wisdom analysis module. 9. A method for quickly identifying the cause of a line fault, it is characterized in that: based on the fault recording system and the server, the fault recording system is connected with the server and communicates unidirectionally, and further comprises the steps, the first step, the fault recording system. The system obtains the fault recorder information at the fault time and sends it to the server, where the fault recorder information includes fault voltage information and fault current information; in the second step, the server receives the fault recorder information at the fault time sent by the fault recorder system, The fault feature quantity is extracted from the fault recorder information, the mapping relationship between the fault feature quantity and the fault cause is established, and the fault feature model is formed; in the third step, the server inputs the acquired fault recorder information into the fault feature model and obtains the corresponding cause of issue. 10. The method for quickly identifying the cause of a line fault according to claim 9, characterized in that: it is also based on a lightning location system, the lightning location system is connected to the server and communicates unidirectionally, and in the first step, the lightning location is The system obtains and sends the lightning strike information to the server. The lightning strike information includes lightning location information and lightning current; in the second step, the server receives the lightning strike information sent by the lightning location system, and extracts the information from the fault recording information and the lightning location information. The fault characteristic quantity is used to establish the mapping relationship between the fault characteristic quantity and the fault cause and form the fault characteristic model; that is, the fault zero-sequence current waveform, the fault phase voltage, the phase current DC content and the harmonic component and the coincidence are extracted from the fault recording information. The gate information is used as the fault numerical feature, and the lightning location information is extracted from the lightning information as the fault numerical feature. The BP neural network is used to establish the mapping relationship between the fault feature and the fault cause, and the fault recording information and the lightning information are used as input vectors. The fault cause is used as the output vector, and the established fault characteristic model is the fault characteristic BP network model; in the third step, the server inputs the acquired fault recording information and lightning information into the fault characteristic model and obtains the corresponding fault cause.

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