CN111880046A - Device and method for quickly identifying line fault reason - Google Patents
Device and method for quickly identifying line fault reason Download PDFInfo
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Abstract
The invention discloses a device and a method for rapidly identifying line fault reasons, and relates to the technical field of power line monitoring; the device comprises a fault recording information acquisition module, a fault characteristic model establishment module and an intelligent analysis module; the method comprises the following steps that firstly, a fault recording system acquires fault recording information at a fault moment and sends the fault recording information to a server, wherein the fault recording information comprises fault voltage information and fault current information; secondly, the server receives fault recording information of the fault moment sent by the fault recording system, extracts fault characteristic quantity from the fault recording information, establishes a mapping relation between the fault characteristic quantity and a fault reason and forms a fault characteristic model; thirdly, the server inputs the acquired fault recording information into a fault characteristic model and acquires a corresponding fault reason; the fault diagnosis method and the fault diagnosis system realize rapid identification of line fault reasons and have high precision by acquiring a fault recording information module, establishing a fault characteristic model module, an intelligent analysis module and the like.
Description
Technical Field
The invention relates to the technical field of power line monitoring, in particular to a device and a method for quickly identifying line fault reasons.
Background
As shown in fig. 5, currently, a power grid dispatching operator mainly depends on information such as voltage, current, switch displacement, protection actions and the like provided by a fault recording system to judge the phase and type of a fault. However, due to functional limitations, the fault recording system cannot quickly provide specific reasons for the circuit switching-off, and the reason for the switching-off can be determined only by means of ranging information provided by the fault recording system after on-site inspection by an inspector, which is long in time consumption.
The fault recording system mainly has the function of providing the information of electric quantities such as voltage and current at the fault time and the variation quantity of switch displacement, protection action and the like, and does not have the functions of analyzing and judging the line fault reason. Therefore, although the voltage, current and other information of the lines with different fault reasons (lightning stroke, foreign matter lap joint and the like) can present different characteristics at the fault moment, due to the function loss, the reason of the brake drop cannot be directly and quickly pushed only by the fault recording system.
According to the on-site line patrol by means of the ranging information, although the reason for the line brake drop can be obtained, the line brake drop needs to be manually carried out, the technical means is lacked, the time consumption is too long, the efficiency is low, the requirement for quickly recovering power transmission is not met, and hidden dangers are brought to the safe operation of a large power grid.
In order to ensure the safety of the power grid, the line fault caused by severe weather such as lightning stroke can be quickly tried out according to the regulation of the power grid operation regulations. If the fault reason can be quickly identified after the line fault by means of technical means, the method is extremely important for quickly recovering the operation of the line and ensuring the safety and stability of a power grid.
Problems with the prior art and considerations:
how to solve the technical problems of low speed and low efficiency of identifying the line fault reason.
Disclosure of Invention
The invention aims to solve the technical problem of providing a device and a method for quickly identifying line fault reasons, which realize quick identification of line fault reasons and have high precision by acquiring a fault recording information module, establishing a fault characteristic model module, an intelligent analysis module and the like.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a device for rapidly identifying line fault causes comprises three program modules including a fault recording information acquisition module, a fault characteristic model establishment module and an intelligent analysis module, wherein the fault recording information acquisition module is used for acquiring fault recording information at a fault moment and supplying the fault recording information to the fault characteristic model establishment module for use, and the fault recording information comprises fault voltage information and fault current information; the fault characteristic model establishing module is used for extracting fault characteristic quantity from the fault recording information, establishing a mapping relation between the fault characteristic quantity and a fault reason and forming a fault characteristic model; and the intelligent analysis module is used for inputting the acquired fault recording information into the fault characteristic model and acquiring a corresponding fault reason.
The further technical scheme is as follows: the fault wave recording system is connected with the server and is in one-way communication with the server to acquire a fault wave recording information module, and the fault wave recording system is also used for acquiring fault wave recording information at a fault moment and sending the fault wave recording information to the server; the fault characteristic model building module is also used for receiving fault recording information of a fault moment sent by a fault recording system by a server and extracting characteristic quantity closely related to the occurrence of the fault from the fault recording information; and the intelligent analysis module is also used for the server to acquire fault recording information, input a fault characteristic model and find out a fault reason.
The further technical scheme is as follows: the lightning information acquisition module is used for acquiring lightning information and providing the lightning information for the fault characteristic model establishment module to use, and the lightning information comprises lightning positioning information and lightning current; the fault characteristic model establishing module is also used for extracting fault characteristic quantity from the fault recording information and the lightning positioning information, establishing a mapping relation between the fault characteristic quantity and a fault reason and forming a fault characteristic model; and the intelligent analysis module is also used for inputting the acquired fault recording information and the acquired thunderbolt information into the fault characteristic model and acquiring a corresponding fault reason.
The further technical scheme is as follows: the lightning positioning system is connected with the server and is in one-way communication with the server, and the lightning positioning information acquisition module is used for acquiring lightning information and sending the lightning information to the server, wherein the lightning information is a lightning place; establishing a fault characteristic model module, wherein the fault characteristic model module is also used for receiving lightning information sent by a lightning positioning system by a server and extracting characteristic quantity closely related to the occurrence of a fault from fault recording information and the lightning positioning information; and the intelligent analysis module is also used for acquiring fault recording information and thunder information by the server, inputting the fault characteristic model and finding out the fault reason.
The further technical scheme is as follows: in a module for establishing a fault characteristic model, the fault characteristic quantity comprises harmonic components in fault voltage, direct-current components in fault current and a lightning landing place; and storing the data of the fault characteristic quantity and the fault reason and forming a fault reason information base.
The further technical scheme is as follows: in the fault characteristic model building module, a fault zero sequence current waveform, a fault phase voltage, a phase current direct-current content, a harmonic component and reclosing information are extracted from fault recording information to serve as fault numerical value characteristics, lightning positioning information is extracted from lightning information to serve as fault numerical value characteristics, a mapping relation between fault characteristic quantity and fault reasons is built by using a BP neural network, the fault recording information and the lightning information serve as input vectors, the fault reasons serve as output vectors, and the built fault characteristic model serves as a fault characteristic BP network model.
The device for rapidly identifying the line fault reason comprises a memory, a processor, and four program modules which are stored in the memory and can run on the processor, namely a fault recording information acquisition module, a thunder and lightning positioning information acquisition module, a fault feature model establishment module and an intelligent analysis module.
The device for rapidly identifying the line fault reason is a computer readable storage medium, wherein four program modules including a fault recording information acquisition module, a thunder and lightning positioning information acquisition module, a fault feature model establishment module and an intelligent analysis module are stored in the computer readable storage medium.
A method for rapidly identifying line fault causes is based on a fault recording system and a server, wherein the fault recording system is connected with the server and is in one-way communication with the server; secondly, the server receives fault recording information of the fault moment sent by the fault recording system, extracts fault characteristic quantity from the fault recording information, establishes a mapping relation between the fault characteristic quantity and a fault reason and forms a fault characteristic model; and thirdly, inputting the acquired fault recording information into a fault characteristic model by the server and acquiring a corresponding fault reason.
The further technical scheme is as follows: the method comprises the following steps that the method further comprises the steps that based on a lightning positioning system, the lightning positioning system is connected with a server and is in one-way communication, in the first step, the lightning positioning system obtains lightning information and sends the lightning information to the server, and the lightning information comprises lightning positioning information and lightning current; in the second step, the server receives the lightning information sent by the lightning positioning system, extracts fault characteristic quantity from the fault recording information and the lightning positioning information, establishes the mapping relation between the fault characteristic quantity and the fault reason and forms a fault characteristic model; extracting fault zero-sequence current waveform, fault phase voltage, phase current direct-current content, harmonic component and reclosing information from fault recording information as fault numerical value characteristics, extracting lightning positioning information from lightning strike information as fault numerical value characteristics, establishing a mapping relation between fault characteristic quantity and fault reason by using a BP (back propagation) neural network, taking the fault recording information and the lightning strike information as input vectors, taking the fault reason as an output vector, and taking an established fault characteristic model as a fault characteristic BP network model; and in the third step, the server inputs the acquired fault recording information and the acquired thunderbolt information into a fault characteristic model and acquires a corresponding fault reason.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
a device for rapidly identifying line fault causes comprises three program modules including a fault recording information acquisition module, a fault characteristic model establishment module and an intelligent analysis module, wherein the fault recording information acquisition module is used for acquiring fault recording information at a fault moment and supplying the fault recording information to the fault characteristic model establishment module for use, and the fault recording information comprises fault voltage information and fault current information; the fault characteristic model establishing module is used for extracting fault characteristic quantity from the fault recording information, establishing a mapping relation between the fault characteristic quantity and a fault reason and forming a fault characteristic model; and the intelligent analysis module is used for inputting the acquired fault recording information into the fault characteristic model and acquiring a corresponding fault reason. The fault diagnosis method and the fault diagnosis system realize rapid identification of line fault reasons and have high precision by acquiring a fault recording information module, establishing a fault characteristic model module, an intelligent analysis module and the like.
The device for rapidly identifying the line fault reason comprises a memory, a processor, and four program modules which are stored in the memory and can run on the processor, namely a fault recording information acquisition module, a thunder and lightning positioning information acquisition module, a fault feature model establishment module and an intelligent analysis module. The fault diagnosis method and the fault diagnosis system realize rapid identification of line fault reasons and have high precision by acquiring a fault recording information module, establishing a fault characteristic model module, an intelligent analysis module and the like.
The device for rapidly identifying the line fault reason is a computer readable storage medium, wherein four program modules including a fault recording information acquisition module, a thunder and lightning positioning information acquisition module, a fault feature model establishment module and an intelligent analysis module are stored in the computer readable storage medium. The fault diagnosis method and the fault diagnosis system realize rapid identification of line fault reasons and have high precision by acquiring a fault recording information module, establishing a fault characteristic model module, an intelligent analysis module and the like.
A method for rapidly identifying line fault causes is based on a fault recording system and a server, wherein the fault recording system is connected with the server and is in one-way communication with the server; secondly, the server receives fault recording information of the fault moment sent by the fault recording system, extracts fault characteristic quantity from the fault recording information, establishes a mapping relation between the fault characteristic quantity and a fault reason and forms a fault characteristic model; and thirdly, inputting the acquired fault recording information into a fault characteristic model by the server and acquiring a corresponding fault reason. Through the steps and the like, the line fault reason can be quickly identified, and the accuracy is high.
See detailed description of the preferred embodiments.
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FIG. 1 is a schematic block diagram of embodiment 1 of the present invention;
FIG. 2 is a schematic block diagram of embodiment 2 of the present invention;
FIG. 3 is a flowchart of embodiment 3 of the present invention;
FIG. 4 is a flowchart of embodiment 4 of the present invention;
fig. 5 is a schematic block diagram of the prior art.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways than those described herein, and it will be apparent to those of ordinary skill in the art that the present application is not limited to the specific embodiments disclosed below.
Example 1:
as shown in fig. 1, the apparatus for rapidly identifying a line fault cause disclosed by the present invention includes a fault recording system for acquiring fault recording information, a server, and three program modules including a module 101 for acquiring fault recording information, a module 102 for establishing a fault feature model, and an intelligent analysis module 103, wherein the fault recording system is connected with the server and is in one-way communication.
The fault recording information obtaining module 101 is configured to obtain fault recording information at a fault moment by a fault recording system and send the fault recording information to a server, where the fault recording information includes fault voltage information, fault current information, and reclosing information.
And a fault characteristic model establishing module 102, configured to receive fault recording information at a fault time sent by a fault recording system, extract a fault characteristic quantity from the fault recording information, establish a mapping relationship between the fault characteristic quantity and a fault reason, and form a fault characteristic model. The method comprises the steps of extracting fault zero-sequence current waveform, fault phase voltage, phase current direct-current content, harmonic component and reclosing information from fault recording information to serve as fault numerical value characteristics, establishing a mapping relation between fault characteristic quantity and fault reasons by using a BP (back propagation) neural network, using the fault recording information as an input vector, using the fault reasons as an output vector, and using an established fault characteristic model as a fault characteristic BP network model.
And the intelligent analysis module 103 is used for the server to obtain the fault recording information, input the fault characteristic model and obtain the corresponding fault reason.
The fault recording system, the server itself and the corresponding communication connection technology are not described herein again for the prior art.
Example 1 illustrates that:
and the server establishes and trains a fault characteristic model by using the historical fault recording information of the fault recording system. When a monitored site has a fault, the fault recording system acquires the fault recording information which just occurs and sends the fault recording information to the server, and the server inputs the newly-occurring fault recording information into the trained fault characteristic model and acquires the corresponding fault reason. The recognizable fault causes comprise bird droppings, and the like.
Example 2:
as shown in fig. 2, the device for rapidly identifying the line fault cause disclosed by the invention comprises a fault recording system for acquiring fault recording information, a lightning positioning system and a server, and four program modules including a fault recording information acquiring module 201, a lightning positioning information acquiring module 202, a fault characteristic model establishing module 203 and an intelligent analysis module 204, wherein the fault recording system is connected with the server and is in one-way communication, and the lightning positioning system is connected with the server and is in one-way communication.
The module 201 for acquiring fault recording information is used for the fault recording system to acquire fault recording information at a fault moment and send the fault recording information to the server, wherein the fault recording information includes fault voltage information, fault current information and reclosing information.
And the thunder and lightning location information obtaining module 202 is used for the thunder and lightning location system to obtain thunder and lightning information and send the thunder and lightning information to the server, wherein the thunder and lightning information comprises thunder and lightning location information and thunder and lightning current, and the thunder and lightning location information is a thunder and lightning place.
And the fault characteristic model establishing module 203 is used for receiving fault recording information at the fault moment sent by the fault recording system and lightning information sent by the lightning positioning system by the server, extracting fault characteristic quantity from the fault recording information and the lightning positioning information, establishing a mapping relation between the fault characteristic quantity and a fault reason and forming a fault characteristic model. The method comprises the steps of extracting fault zero-sequence current waveform, fault phase voltage, phase current direct-current content, harmonic component and reclosing information from fault recording information to serve as fault numerical characteristics, extracting lightning locating information from lightning information to serve as fault numerical characteristics, establishing a mapping relation between fault characteristic quantity and fault reasons by using a BP neural network, taking the fault recording information and the lightning information as input vectors, taking the fault reasons as output vectors, and establishing a fault characteristic model as a fault characteristic BP network model.
And the intelligent analysis module 204 is used for the server to obtain the fault recording information and the thunderbolt information, input the fault characteristic model and obtain the corresponding fault reason.
The fault recording system, the lightning positioning system, the server and the corresponding communication connection technology are not described herein in detail for the prior art.
Example 2 illustrates that:
and the server establishes and trains a fault characteristic model by using the historical fault recording information of the fault recording system and the historical lightning falling information of the lightning positioning system. When a monitored site has a fault, the fault recording system acquires fault recording information which just occurs and sends the fault recording information to the server, the thunder and lightning positioning system acquires thunder information which just occurs and sends the thunder information to the server, and the server inputs the newly-occurring fault recording information and the thunder information into a trained fault characteristic model and obtains a corresponding fault reason. The identifiable cause of the fault further includes a cause of a lightning fault.
Example 3:
as shown in fig. 3, the present invention discloses a method for quickly identifying a cause of a line fault, which is based on a hardware device of embodiment 1, and includes the following steps:
s101, acquiring fault recording information
The fault recording system acquires fault recording information at a fault moment and sends the fault recording information to the server, wherein the fault recording information comprises fault voltage information and fault current information.
S102, establishing a fault characteristic model
The server receives fault recording information of a fault moment sent by a fault recording system, extracts fault characteristic quantity from the fault recording information, establishes a mapping relation between the fault characteristic quantity and a fault reason and forms a fault characteristic model.
The method comprises the steps of extracting fault zero-sequence current waveform, fault phase voltage, phase current direct-current content, harmonic component and reclosing information from fault recording information to serve as fault numerical value characteristics, establishing a mapping relation between fault characteristic quantity and fault reasons by using a BP (back propagation) neural network, using the fault recording information as an input vector, using the fault reasons as an output vector, and using an established fault characteristic model as a fault characteristic BP network model.
S103 Intelligent analysis
And the server inputs the acquired fault recording information into a fault characteristic model and acquires a corresponding fault reason.
Example 4:
as shown in fig. 4, the present invention discloses a method for quickly identifying a cause of a line fault, which is based on a hardware device of embodiment 2, and includes the following steps:
s201 acquiring fault recording information and thunder and lightning positioning information
The lightning positioning system acquires lightning information and sends the lightning information to the server, wherein the lightning information comprises lightning positioning information and lightning current; the fault recording system acquires fault recording information at a fault moment and sends the fault recording information to the server, wherein the fault recording information comprises fault voltage information and fault current information. The thunder and lightning positioning system and the fault recording system are independent from each other, and no fixed sequence exists.
S202, establishing a fault characteristic model
The server receives fault recording information of a fault moment sent by the fault recording system and lightning information sent by the lightning positioning system, extracts fault characteristic quantity from the fault recording information and the lightning positioning information, establishes a mapping relation between the fault characteristic quantity and a fault reason and forms a fault characteristic model.
The method comprises the steps of extracting fault zero-sequence current waveform, fault phase voltage, phase current direct-current content, harmonic component and reclosing information from fault recording information to serve as fault numerical characteristics, extracting lightning locating information from lightning information to serve as fault numerical characteristics, establishing a mapping relation between fault characteristic quantity and fault reasons by using a BP neural network, taking the fault recording information and the lightning information as input vectors, taking the fault reasons as output vectors, and establishing a fault characteristic model as a fault characteristic BP network model.
S203 Intelligent analysis
And the server inputs the acquired fault recording information and the acquired thunderbolt information into a fault characteristic model and acquires a corresponding fault reason.
Example 5:
the invention discloses a device for quickly identifying the cause of a line fault, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the steps of embodiment 3 are realized when the processor executes the computer program.
Example 6:
a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps in embodiment 4 is disclosed.
The invention concept of the application is as follows:
based on a fault wave recording system and a server, the following steps are executed through a fault wave recording information obtaining module, a fault characteristic model establishing module and an intelligent analysis module, fault wave recording information is obtained, and the fault wave recording system obtains fault wave recording information at a fault moment and sends the fault wave recording information to the server; establishing a fault characteristic model, receiving fault recording information of a fault moment sent by a fault recording system by a server, extracting fault characteristic quantity from the fault recording information, establishing a mapping relation between the fault characteristic quantity and a fault reason and forming the fault characteristic model; intelligent analysis, namely inputting the acquired fault recording information into a fault characteristic model by the server and acquiring a corresponding fault reason; the method and the device realize quick identification of line fault reasons and have high precision.
Based on a fault recording system, a thunder positioning system and a server, the following steps are executed through a fault recording information obtaining module, a thunder positioning information obtaining module, a fault characteristic model establishing module and an intelligent analysis module, fault recording information is obtained, and the fault recording system obtains fault recording information at a fault moment and sends the fault recording information to the server; acquiring lightning positioning information, and acquiring lightning information and sending the information to a server by a lightning positioning system; establishing a fault characteristic model, receiving fault recording information at a fault moment sent by a fault recording system and lightning falling information sent by a lightning positioning system by a server, extracting fault characteristic quantities from the fault recording information and the lightning positioning information, establishing a mapping relation between the fault characteristic quantities and fault reasons and forming the fault characteristic model; intelligent analysis, wherein the server inputs the acquired fault recording information and the acquired thunderbolt information into a fault characteristic model and acquires a corresponding fault reason; and the reason of the line fault is further quickly identified, and the accuracy is high.
Description of the technical solution:
as shown in fig. 2, the technical solution of the present application adds four program modules based on the prior art solution (fault recording system): the device comprises a fault recording information acquisition module, a thunder and lightning positioning information acquisition module, a fault characteristic model establishment module and an intelligent analysis module.
1. Module for acquiring fault recording information
And extracting voltage and current information, thunder and lightning positioning and other information at the fault moment in the fault recording system.
Extracting line fault characteristic quantity:
after a line fault occurs, voltage and current information of a fault moment in the fault recording system and relevant lightning information in the lightning positioning system are automatically pushed to the intelligent analysis module. And extracting characteristic quantities closely related to the occurrence of the fault, such as harmonic components in fault voltage, direct current components in fault current, lightning-down places and the like from the fault recording information and the lightning positioning information.
The numerical characteristics of the transmission line fault in the flashover process can be divided into two types. One type is fault information directly observed from a oscillogram, including: the amplitude and phase of each phase voltage and current after flashover, the change of fault waveform, the reclosing condition after fault tripping and the like. The other type is numerical characteristics obtained by calculation and derivation of fault recording data, such as fault phase voltage, phase current direct-current content and harmonic component, and for fault waveforms with wave forms of non-standard sine, a Fourier series expansion method can be used for decomposing the fault waveforms into a series of sine quantity sums with frequencies of positive integral multiples of power frequency. Therefore, the characteristic analysis and extraction of the direct current content and the harmonic component of the fault waveform are realized.
2. Module for acquiring lightning positioning information
The module can acquire the thunderbolt information provided by the thunder and lightning positioning system: a lightning landing place (can be detailed to a line tower), lightning current and the like.
3. Module for establishing fault feature model
Based on the failure mechanism and the deep analysis of a large amount of actual failure data, the characteristic quantities closely related to the occurrence of the failure are extracted from the failure recording information and the lightning positioning information, and the effective relation between the failure characteristic quantities and the failure reasons is established as the identification basis.
Establishing a fault reason information base:
based on the failure mechanism and the deep analysis of a large amount of actual failure data, typical characteristic quantities of failures of different reasons are summarized, and all characteristic information is stored in a failure reason information base.
4. Intelligent analysis module
When the cause of a specific fault is identified, only relevant features of the fault are extracted and a known fault cause identification basis is referred, so that a fault cause with the strongest corresponding relation can be deduced and automatically pushed.
Identifying a fault reason:
according to the principle that the fault characteristic quantity is closely related to the fault cause type and can be effectively extracted and calculated, a fault zero-sequence current waveform, a fault phase voltage, a phase current direct-current content, a harmonic component and reclosing information are extracted from fault recording information and serve as effective fault numerical characteristics, and lightning positioning information is supplemented. The advantage that the BP neural network can establish nonlinear mapping between input and output is utilized, fault recording information and lightning information are used as input vectors, fault reasons are used as output vectors, and a plurality of fault characteristic BP network models are established. The BP network algorithm after sample training can be used for identifying the fault reason.
For example, there are three characteristics in lightning fault:
(1) voltage glitch: the intelligent module decomposes and analyzes the waveform to determine whether harmonic components exist;
(2) current sine wave, almost free of high frequency harmonics: the intelligent module decomposes the waveform to obtain a conclusion that only the sine wave exists;
(3) the fault moment is large fault impulse current, so that abundant attenuated direct current components appear: the intelligent module captures and extracts the characteristic of the direct current component. And comparing the lightning stroke fault with the existing information base to obtain the lightning stroke fault with higher probability.
Line fault characteristic quantity summarization:
1. electrical quantity characteristic when the lightning stroke breaks the brake:
(1) in the initial stage of flashover, the lightning current wave head is rapidly attenuated, so that the voltage fluctuation of a fault phase is large, and the waveform of the initial voltage appears burr-type disturbance.
(2) The lightning causes the insulation breakdown flashover of the conducting wire and the ground (the ground wire or the tower), then the power frequency voltage is discharged continuously along the flashover channel to be developed into a power frequency arc, the arc resistor and the tower resistor have linear volt-ampere characteristics, and the waveform of the fault phase voltage is gradually changed into a regular and stable sine wave.
(3) Due to the invasion of lightning current, a circuit can generate large short-circuit impact current at the moment of fault, so that after flashover, a fault phase current and a zero sequence current contain large attenuation direct current components under the condition of more faults, and the waveform of the fault current is mostly an obvious asymmetric sine wave.
(4) Lightning faults can generally be coincidental to success.
2. Electrical quantity characteristic when the gate is dropped to the bird pest:
(1) nature of fault grounding
The bird trouble flashover channel is air arc and bird droppings with high conductivity, and belongs to the similar metallic fault.
(2) Fault recording signature
The grounding resistor has linear volt-ampere characteristics, the fault current waveform is mostly sine wave, and the grounding resistor hardly contains attenuation direct current components and high-frequency harmonic components. And (4) statistically analyzing the voltage waveform of the bird trouble fault phase, and finding that the voltage waveform of the bird trouble fault phase is mostly a sine wave (obviously different from the line collision fault of the crane).
(3) The probability of successful reclosing of the bird trouble fault is high.
3. Electric quantity characteristic when the crane meets and breaks off the brake:
(1) nature of fault grounding
The crane wire-touch fault is air breakdown caused by the approach of a metal car body to a power transmission line, and the line is grounded with the car body along an air arc channel. The crane arm is generally a metal conductor, has small and fixed resistance, and the stable burning electric arc is linear low resistance.
(2) Fault recording signature
Because the volt-ampere characteristic of the transition resistor is linear, the fault waveform is sinusoidal and contains less attenuated direct-current components and high-frequency harmonic components. And looking up the phase voltage waveform of the line-touching fault of the crane, and finding that the phase voltage waveform of the line-touching fault of the crane is mostly irregular sine wave.
(3) When a crane is in line contact with a power transmission line to trip, a suspension arm cannot be quickly moved away, and when the crane is reclosed, a short-circuit channel still exists, the power transmission line can be tripped again, so that fault reclosing is often unsuccessful.
4. Electric quantity characteristic when the floodgate is fallen in the foreign matter overlap joint:
(1) nature of fault grounding
The wiring fault of the nonmetallic foreign matters is caused by the fact that objects such as trees, kites and the like are close to the conducting wires, and the lines discharge to the objects. During flashover, the high temperatures generated by the short circuit current may cause the object to burn, resulting in the flashover path exhibiting non-linear, random variations. Therefore, the resistance value of the transition resistor in the wiring fault of the nonmetallic foreign matter is larger than that of the metallic resistor, and the volt-ampere characteristic of the transition resistor has certain nonlinear characteristics.
(2) Fault recording signature
Due to the fact that the resistance value of the transition resistor is large and nonlinear, the voltage of the fault phase is not changed greatly after flashover, the fault phase current and the zero sequence current are distorted in waveform and contain high-frequency harmonic components.
The foreign matter wiring causes the tripping operation of the power transmission line, and the high temperature generated by the short-circuit current can fuse or burn the contact part of the wiring object and the discharge channel under the common condition, so that the air distance between the lead and the object is increased. Therefore, reclosing is easy to succeed.
Table 1: characteristic library of fault electric quantities of different reasons
After the application runs secretly for a period of time, the feedback of field technicians has the advantages that:
after the line fault, the fault reason can be pushed together with the fault phase and type. After the reason of line fault and brake drop is mastered, the situations that tower collapse, line breakage, permanent grounding and the like do not have power transmission recovery can be eliminated, powerful support is provided for dispatchers to quickly test power transmission, especially under severe weather, great significance is brought to the safety guarantee of line quick transmission and power grid racks, the risk time of 220kV and above transformer substation full stop, large-area power failure of a main grid rack and the like can be effectively shortened, and powerful guarantee is provided for social industry, resident life persistence and high-quality power utilization.
Meanwhile, the reason causing the fault can be quickly judged, time is saved for searching fault points, secondarily determining the fault reason and eliminating the defects of line patrol personnel, and the working efficiency is greatly improved.
Claims (10)
1. A device for rapidly identifying line fault causes is characterized in that: the fault recording method comprises a fault recording information obtaining module, a fault characteristic model establishing module and an intelligent analysis module, wherein the three program modules are the three program modules, the fault recording information obtaining module is used for obtaining fault recording information at a fault moment and supplying the fault recording information to the fault characteristic model establishing module for use, and the fault recording information comprises fault voltage information and fault current information; the fault characteristic model establishing module is used for extracting fault characteristic quantity from the fault recording information, establishing a mapping relation between the fault characteristic quantity and a fault reason and forming a fault characteristic model; and the intelligent analysis module is used for inputting the acquired fault recording information into the fault characteristic model and acquiring a corresponding fault reason.
2. The apparatus for rapidly identifying the cause of the line fault according to claim 1, wherein: the fault wave recording system is connected with the server and is in one-way communication with the server to acquire a fault wave recording information module, and the fault wave recording system is also used for acquiring fault wave recording information at a fault moment and sending the fault wave recording information to the server; the fault characteristic model building module is also used for receiving fault recording information of a fault moment sent by a fault recording system by a server and extracting characteristic quantity closely related to the occurrence of the fault from the fault recording information; and the intelligent analysis module is also used for the server to acquire fault recording information, input a fault characteristic model and find out a fault reason.
3. The apparatus for rapidly identifying the cause of the line fault according to claim 1, wherein: the lightning information acquisition module is used for acquiring lightning information and providing the lightning information for the fault characteristic model establishment module to use, and the lightning information comprises lightning positioning information and lightning current; the fault characteristic model establishing module is also used for extracting fault characteristic quantity from the fault recording information and the lightning positioning information, establishing a mapping relation between the fault characteristic quantity and a fault reason and forming a fault characteristic model; and the intelligent analysis module is also used for inputting the acquired fault recording information and the acquired thunderbolt information into the fault characteristic model and acquiring a corresponding fault reason.
4. The apparatus for rapidly identifying the cause of the line fault according to claim 2, wherein: the lightning positioning system is connected with the server and is in one-way communication with the server, and the lightning positioning information acquisition module is used for acquiring lightning information and sending the lightning information to the server, wherein the lightning information is a lightning place; establishing a fault characteristic model module, wherein the fault characteristic model module is also used for receiving lightning information sent by a lightning positioning system by a server and extracting characteristic quantity closely related to the occurrence of a fault from fault recording information and the lightning positioning information; and the intelligent analysis module is also used for acquiring fault recording information and thunder information by the server, inputting the fault characteristic model and finding out the fault reason.
5. The apparatus for rapidly identifying the cause of the line fault according to claim 3, wherein: in a module for establishing a fault characteristic model, the fault characteristic quantity comprises harmonic components in fault voltage, direct-current components in fault current and a lightning landing place; and storing the data of the fault characteristic quantity and the fault reason and forming a fault reason information base.
6. The apparatus for rapidly identifying the cause of the line fault according to claim 3, wherein: in the fault characteristic model building module, a fault zero sequence current waveform, a fault phase voltage, a phase current direct-current content, a harmonic component and reclosing information are extracted from fault recording information to serve as fault numerical value characteristics, lightning positioning information is extracted from lightning information to serve as fault numerical value characteristics, a mapping relation between fault characteristic quantity and fault reasons is built by using a BP neural network, the fault recording information and the lightning information serve as input vectors, the fault reasons serve as output vectors, and the built fault characteristic model serves as a fault characteristic BP network model.
7. An apparatus for rapidly identifying a cause of a line fault, comprising a memory and a processor, wherein: the intelligent lightning fault diagnosis system also comprises four program modules which are stored in the memory and can run on the processor, namely a fault recording information acquisition module, a lightning positioning information acquisition module, a fault feature model establishment module and an intelligent analysis module.
8. An apparatus for quickly identifying a cause of a line fault, which is a computer-readable storage medium, characterized in that: the computer readable storage medium stores four program modules including a fault recording information acquisition module, a thunder and lightning positioning information acquisition module, a fault feature model establishment module and an intelligent analysis module.
9. A method for rapidly identifying the reason of line fault is characterized in that: based on a fault wave recording system and a server, the fault wave recording system is connected with the server and is in one-way communication with the server, and the fault wave recording system also comprises the following steps of firstly, acquiring fault wave recording information at a fault moment by the fault wave recording system and sending the fault wave recording information to the server, wherein the fault wave recording information comprises fault voltage information and fault current information; secondly, the server receives fault recording information of the fault moment sent by the fault recording system, extracts fault characteristic quantity from the fault recording information, establishes a mapping relation between the fault characteristic quantity and a fault reason and forms a fault characteristic model; and thirdly, inputting the acquired fault recording information into a fault characteristic model by the server and acquiring a corresponding fault reason.
10. The method for rapidly identifying the cause of the line fault according to claim 9, wherein: the method comprises the following steps that the method further comprises the steps that based on a lightning positioning system, the lightning positioning system is connected with a server and is in one-way communication, in the first step, the lightning positioning system obtains lightning information and sends the lightning information to the server, and the lightning information comprises lightning positioning information and lightning current; in the second step, the server receives the lightning information sent by the lightning positioning system, extracts fault characteristic quantity from the fault recording information and the lightning positioning information, establishes the mapping relation between the fault characteristic quantity and the fault reason and forms a fault characteristic model; extracting fault zero-sequence current waveform, fault phase voltage, phase current direct-current content, harmonic component and reclosing information from fault recording information as fault numerical value characteristics, extracting lightning positioning information from lightning strike information as fault numerical value characteristics, establishing a mapping relation between fault characteristic quantity and fault reason by using a BP (back propagation) neural network, taking the fault recording information and the lightning strike information as input vectors, taking the fault reason as an output vector, and taking an established fault characteristic model as a fault characteristic BP network model; and in the third step, the server inputs the acquired fault recording information and the acquired thunderbolt information into a fault characteristic model and acquires a corresponding fault reason.
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