CN114113911A - Fault waveform-based fault type discrimination method and discrimination system - Google Patents

Fault waveform-based fault type discrimination method and discrimination system Download PDF

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CN114113911A
CN114113911A CN202111475878.7A CN202111475878A CN114113911A CN 114113911 A CN114113911 A CN 114113911A CN 202111475878 A CN202111475878 A CN 202111475878A CN 114113911 A CN114113911 A CN 114113911A
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fault
waveform
fault type
time
type
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CN114113911B (en
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周超
刘辉
贾然
张洋
刘嵘
沈浩
刘传彬
赵国
秦佳峰
李盈盈
孙晓斌
李丹丹
李珊
高成成
蔡英明
张华健
陈新
胡德良
于国强
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

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Abstract

本发明涉及故障判别技术领域，公开一种基于故障波形的故障类型判别方法。一种基于故障波形的故障类型判别方法，包括以下步骤：根据故障录波数据，获得故障波形图；根据故障波形图，判断线路的故障类型。本发明实施例的基于故障波形的故障类型判别方法降低了人工计算难度，缩短了故障处理的时间，提高了对故障处理的效率。本发明实施例还公开了一种基于故障波形的故障类型判别系统。

Figure 202111475878

The invention relates to the technical field of fault discrimination, and discloses a fault type discrimination method based on a fault waveform. A fault type discrimination method based on fault waveform, comprising the following steps: obtaining a fault waveform diagram according to the fault recording data; and judging the fault type of the line according to the fault waveform diagram. The fault type discrimination method based on the fault waveform in the embodiment of the present invention reduces the difficulty of manual calculation, shortens the time for fault processing, and improves the efficiency of fault processing. The embodiment of the present invention also discloses a fault type discrimination system based on the fault waveform.

Figure 202111475878

Description

Fault waveform-based fault type discrimination method and discrimination system
Technical Field
The invention relates to the technical field of fault type judgment, in particular to a fault type judgment method and a fault type judgment system based on fault waveforms.
Background
The overhead transmission line is an important component of the power system, the safety state of the overhead transmission line is very important in the safe and stable operation of the line, and the fact that the fault type can be quickly judged when the overhead transmission line breaks down has important significance in efficient, stable and safe operation of the power system. At present, when a fault occurs, the type of the fault is judged by manual inspection, the consumed time is long, the reaction speed is low, and the efficiency is low.
Therefore, how to provide a method capable of improving the efficiency of determining the fault type is an urgent problem to be solved at present.
Disclosure of Invention
The embodiment of the invention provides a fault type judging method and a fault type judging system based on fault waveforms, and aims to solve the problems that the type of a fault is judged by manual inspection and the efficiency is low in the prior art. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to a first aspect of the embodiments of the present invention, a fault type discrimination method based on a fault waveform is provided.
In one embodiment, the fault type discrimination method based on the fault waveform includes the following steps:
acquiring a fault oscillogram according to the fault recording data;
and judging the fault type of the line according to the fault oscillogram.
Optionally, the step of obtaining the fault oscillogram according to the fault recording data includes:
step (A), according to the fault recording data, obtaining the starting time and the ending time of the fault;
and (B) acquiring a fault oscillogram according to the fault starting time and the fault ending time.
Optionally, in the step (a), the step of obtaining the fault starting time according to the fault recording data includes:
when the sudden change of current starting meets the starting criterion, the starting criterion is judged to be the fault starting moment, and the criterion formula is as follows:
|Δi(n)|＝|i(n)-i(n-N)|＞ΔIs.set(formula 1)
In the formula,. DELTA.Is.setSetting value, Δ I, representing a sudden variable start judgments.set>0；
N represents the sampling times of each cycle;
n represents a time node, i (n) represents a current value of the time node;
Figure BDA0003393378020000021
in the formula, kdRepresenting a set value coefficient of a break variable starting criterion;
IA0representing the steady-state amplitude of the A-phase current;
IB0representing the steady-state amplitude of the B-phase current;
IC0representing the steady state magnitude of the C-phase current.
Optionally, in the step (a), the step of obtaining the fault termination time according to the fault recording data includes:
if in a period, all sampling points of the period simultaneously meet the condition that the absolute values of fault components in the period are all smaller than half of the load current amplitude, the current amplitudes of fault phases in the period are all smaller than the load current amplitude, and the starting time of the period is pushed forward by a period, namely the fault termination time.
Optionally, in the step (B), obtaining a fault waveform diagram according to the starting time and the ending time of the fault, including:
and obtaining the ABC three-phase voltage and a waveform diagram within the fault occurrence time according to the fault starting time and the fault ending time.
Optionally, the determining the fault type of the line according to the fault oscillogram includes:
and calculating the times of the jumping period To of the ABC three-phase voltage and the waveform within fixed time according To the ABC three-phase voltage and the waveform diagram within the fault occurrence time, and judging the fault type according To the times of the jumping period To.
Optionally, in the step (B), obtaining a fault waveform diagram according to the starting time and the ending time of the fault, including:
calculating the fault distance by using a lumped parameter method:
Figure BDA0003393378020000031
Figure BDA0003393378020000032
in the formula, DmFIs the distance to failure; f is a fault point; m and n are bus positions at two ends respectively; j is a sequence network number, is determined by the fault type, and is 1 for the three-phase short circuit J; for two-phase short circuit, J is 1, 2; short to ground, J ═ 1,2, 0; zJRepresenting an impedance; dLRepresents the total length of the line; dnFRepresents the distance from n to F;
Figure BDA0003393378020000033
the sequence voltages representing the fault point F;
Figure BDA0003393378020000034
represents the voltage measured by the terminal M;
Figure BDA0003393378020000035
represents the voltage measured at the N terminal;
Figure BDA0003393378020000036
represents the current measured by the M terminal;
Figure BDA0003393378020000037
represents the current measured by the N terminal;
coupled 2, 3 cancellation
Figure BDA0003393378020000038
Obtaining:
Figure BDA0003393378020000039
obtaining DmFThen, the sequence voltage U of the fault point F obtained by the formula (2) is combinedFJCalculating each sequence component of the short-circuit current at the fault point according to the following formula;
Figure BDA00033933780200000310
obtaining the transition resistance RFThe following formula:
Figure BDA0003393378020000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003393378020000042
in order to be the fault point current,
Figure BDA0003393378020000043
is the fault point voltage;
according to transition resistance RFAnd drawing a fault waveform chart of the fault occurrence time.
Optionally, the determining the fault type of the line according to the fault oscillogram includes:
using an image recognition algorithm, will depend on the transition resistance RFAnd matching the drawn fault waveform image with a defined fault waveform image in a fault history library, and judging the fault type of the line.
Optionally, the image recognition algorithm comprises:
dividing the fault oscillogram into small areas;
generating a prior frame through a target detection algorithm;
screening the prior frames meeting the conditions, and outputting confidence frame;
and inputting the confidence picture frame into a convolutional neural network for deep learning to obtain a fault type judgment result.
Optionally, the method further includes a step of verifying the fault type determination result, specifically:
calculating the frequency of the jumping period of the ABC three-phase voltage and the waveform within fixed time according to the ABC three-phase voltage and the waveform diagram within the fault occurrence time, and judging the fault type according to the frequency of the jumping period;
if the results are the same, returning the judged fault type;
if the results are different, calling a dynamic thematic map of the longitude and latitude position of the fault point, and returning the fault type consistent with the judgment result of the dynamic thematic map based on the judgment result of the dynamic thematic map; if the dynamic thematic map is not changed, returning the judgment result of the original fault type.
According to a second aspect of the embodiments of the present invention, there is provided a fault type discrimination system based on a fault waveform.
In one embodiment, the fault type discrimination system based on fault waveform includes:
the oscillogram drawing module is used for obtaining a fault oscillogram according to the fault recording data;
and the fault type judging module is used for judging the fault type of the line according to the fault oscillogram.
Optionally, the waveform drawing module is specifically configured to:
acquiring the starting time and the ending time of the fault according to the fault recording data;
and obtaining a fault oscillogram according to the starting time and the ending time of the fault.
Optionally, the fault type determining module is specifically configured to:
and matching the fault waveform image with a defined fault waveform image in a fault history library by using an image recognition algorithm, and judging the fault type of the line.
Optionally, the image recognition algorithm comprises:
dividing the fault oscillogram into small areas;
generating a prior frame through a target detection algorithm;
screening the prior frames meeting the conditions, and outputting confidence frame;
and inputting the confidence picture frame into a convolutional neural network for deep learning to obtain a fault type judgment result.
Optionally, the system further includes a verification module, configured to verify the fault type determination result output by the fault type determination module, where the verification module includes:
calculating the frequency of the jumping period of the ABC three-phase voltage and the waveform within fixed time according to the ABC three-phase voltage and the waveform diagram within the fault occurrence time, and judging the fault type according to the frequency of the jumping period;
if the results are the same, returning the judged fault type;
if the results are different, calling a dynamic thematic map of the longitude and latitude position of the fault point, and returning the fault type consistent with the judgment result of the dynamic thematic map based on the judgment result of the dynamic thematic map; if the dynamic thematic map is not changed, returning the judgment result of the original fault type.
According to a third aspect of embodiments of the present invention, there is provided a computer apparatus.
In some embodiments, the computer device comprises a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the data in the fault recorder is processed and analyzed, and a fault oscillogram is established by analyzing the fault recorder data, so that the fault type of the line is judged, the manual calculation difficulty is reduced, the fault processing time is shortened, and the fault processing efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram illustrating a fault waveform based fault type discrimination method according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating a fault waveform based fault type discrimination system in accordance with an exemplary embodiment;
FIG. 3 is a schematic diagram illustrating the structure of a computer device according to an example embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments herein includes the full ambit of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like, herein are used solely to distinguish one element from another without requiring or implying any actual such relationship or order between such elements. In practice, a first element can also be referred to as a second element, and vice versa. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a structure, device or apparatus that comprises the element. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein, as used herein, are defined as orientations or positional relationships based on the orientation or positional relationship shown in the drawings, and are used for convenience in describing and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may include, for example, mechanical or electrical connections, communications between two elements, direct connections, and indirect connections via intermediary media, where the specific meaning of the terms is understood by those skilled in the art as appropriate.
Herein, the term "plurality" means two or more, unless otherwise specified.
Herein, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an associative relationship describing objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
Fig. 1 shows an embodiment of a fault type discrimination method based on fault waveforms of the present invention.
In this optional embodiment, the fault type determination method based on the fault waveform includes the following steps:
step S1, obtaining a fault oscillogram according to the fault recording data;
and step S2, judging the fault type of the line according to the fault oscillogram.
The fault type judging method based on the fault waveform of the embodiment of the invention processes and analyzes the fault recording data in the fault recorder, thereby judging the fault type of the line.
The fault recording data comprises fault types, fault occurrence time, current and voltage change processes, relay protection and action conditions of automatic devices. When the system is abnormal or has faults, the fault recorder automatically records the fault type, the fault occurrence time, the current and voltage change process and the action conditions of the relay protection and the automatic device, and for three-phase electricity, fault recording data comprises the phase position of the fault. The fault recording data type is mainly in a comtrade format, and an effective means is provided for judging and analyzing the nature of the complex fault by analyzing the fault recording data.
In one embodiment, the step of obtaining the fault waveform map according to the fault recording data includes:
step (A), according to the fault recording data, obtaining the starting time and the ending time of the fault;
and (B) acquiring a fault oscillogram according to the fault starting time and the fault ending time.
Optionally, in the step (a), the step of obtaining the fault starting time according to the fault recording data includes:
when the sudden change of current starting meets the starting criterion, the starting criterion is judged to be the fault starting moment, and the criterion formula is as follows:
|Δi(n)|＝|i(n)-i(n-N)|＞ΔIs.set(formula 1)
In the formula,. DELTA.Is.setSetting value, Δ I, representing a sudden variable start judgments.set>0；
N represents the sampling times of each cycle;
n represents a time node, i (n) represents a current value of the time node;
Figure BDA0003393378020000081
in the formula, kdRepresenting a set value coefficient of a break variable starting criterion;
IA0representing the steady-state amplitude of the A-phase current;
IB0representing the steady-state amplitude of the B-phase current;
IC0representing the steady state magnitude of the C-phase current.
After obtaining the fault recording data of the fault recorder, judging the starting time of the fault, and aiming at: on one hand, the analysis time interval of the distance measurement and the transition resistance can be determined only by determining the starting moment of the fault; on the other hand, in calculating the fault distance and the transition resistance, the time synchronization of both ends is to be maintained. In normal conditions, the fault moments at two ends of the line are the same (traveling wave time difference can be ignored in power frequency analysis), but fault recording at two ends of the line possibly fails to time, so that the fault starting moments at two ends are calculated respectively, when the fault starting moments at two ends are different, the time difference caused by failure of the fault recorder due to time synchronization is considered, and the fault starting moments at two ends are aligned to the same moment for analysis.
Optionally, in the step (a), the step of obtaining the fault termination time according to the fault recording data includes:
if in a period, all sampling points of the period simultaneously meet the condition that the absolute values of fault components in the period are all smaller than half of the load current amplitude, the current amplitudes of fault phases in the period are all smaller than the load current amplitude, and the starting time of the period is pushed forward by a period, namely the fault termination time.
A section of complete fault oscillogram can be obtained by calibrating the starting time and the ending time of fault recording, and the diagnosis of line fault reasons can be carried out by analyzing the fault oscillogram.
In one embodiment, the step (B), obtaining a fault waveform diagram according to the fault starting time and the fault ending time, includes:
and obtaining the ABC three-phase voltage sum (UO) waveform diagram within the fault occurrence time according to the fault starting time and the fault ending time.
Next, the Uo waveform is analyzed, the fault starting time and the fault ending time are obtained, so that the fault occurrence time is clear, the number of times of the jumping period To of the Uo waveform within a fixed time (for example, 15ms) is calculated according To the Uo waveform diagram within the fault occurrence time, and the fault type is judged according To the number of times of the jumping period To. And the voltage value is regarded as a jumping period To when rising and falling, different fault types are realized, the times of the jumping period To in the UO waveform period are different, and the fault type can be judged according To the times of the jumping period To in the UO waveform period.
And (3) according To the actually occurring fault reasons, carrying out set division on various UO waveforms such as bird damage, windage yaw, icing, lightning stroke, external force damage and the like, and calculating the To number within 15ms of the starting time of each set fault. Through calculation, the number of the faults To of bird damage and windage yaw is obviously higher than that of other fault types such as icing, lightning stroke, external force damage and the like. Taking bird damage and windage yaw as examples, the fault period of bird damage and windage yaw can be recovered To normal within 60ms, the number of the To faults of bird damage is greater than that of windage yaw faults, wherein the To value interval of birds is [9,20], and the To value interval of windage yaw is [4,8], so that the fault type can be judged To be windage yaw fault if the To value of the fault oscillogram is greater than 9, and the fault type can be judged To be windage yaw fault if the To value of the fault oscillogram is within the To value interval of windage yaw fault.
In another embodiment, the step (B), obtaining the fault waveform map according to the fault starting time and the fault ending time, includes:
calculating the fault distance by using a lumped parameter method:
Figure BDA0003393378020000101
Figure BDA0003393378020000102
in the formula, DmFIs the distance to failure; f is a fault point; m and n are bus positions at two ends respectively; j is a sequence network number, is determined by the fault type, and is 1 for the three-phase short circuit J; for two-phase short circuit, J is 1, 2; short to ground, J ═ 1,2, 0; zJRepresenting an impedance; dLRepresents the total length of the line; dnFRepresents the distance from n to F;
Figure BDA0003393378020000103
the sequence voltages representing the fault point F;
Figure BDA0003393378020000104
represents the voltage measured by the terminal M;
Figure BDA0003393378020000105
represents the voltage measured at the N terminal;
Figure BDA0003393378020000106
represents the current measured by the M terminal;
Figure BDA0003393378020000107
represents the current measured by the N terminal;
coupled 2, 3 cancellation
Figure BDA0003393378020000108
Obtaining:
Figure BDA0003393378020000109
obtaining DmFThen, the sequence voltage U of the fault point F obtained by the formula (2) is combinedFJCalculating each sequence component of the short-circuit current at the fault point according to the following formula;
Figure BDA00033933780200001010
obtaining the transition resistance RFThe following formula:
Figure BDA0003393378020000111
in the formula (I), the compound is shown in the specification,
Figure BDA0003393378020000112
in order to be the fault point current,
Figure BDA0003393378020000113
is the fault point voltage;
according to transition resistance RFAnd drawing a fault waveform chart between the starting time and the ending time of the fault (namely the fault occurrence time).
Next, using an image recognition algorithm, the transition resistance R will be basedFThe drawn fault waveform image is matched with a defined fault waveform image in a fault history library,and judging the fault type of the line. For example, through the above calculation process, R of the fault such as bird damage, windage yaw, icing, lightning stroke, external force damage, etc. is obtainedFAnd data are stored, a fault waveform diagram of the fault is drawn, the drawn fault waveform diagram is matched with a defined fault waveform image in a fault history library by using an image recognition algorithm, and a most similar history picture is found according to image characteristics, so that the fault type is judged.
The image recognition algorithm comprises:
step S101, dividing the fault waveform map into small areas.
First, a plurality of small areas are generated on the fault waveform image by using an image segmentation algorithm, and the areas are the most basic sub-areas. And carrying out region combination according to the similarity between the regions, determining a standard for measuring the similarity, continuously overlapping small regions until all the small regions are combined together, and then making a circumscribed rectangle for each region to obtain a region box which can be an object.
And S102, generating a prior frame through a target detection algorithm.
The method comprises the steps of adopting a target detection algorithm, such as yolov5 algorithm, using a Mosaic enhancement method at an input end, splicing a plurality of pictures, such as 4 pictures according to a random scaling, random cutting and random arrangement mode, then carrying out self-adaptive anchor frame calculation, wherein a model generates a plurality of prior frames during each training.
Step S103, screening the prior frames meeting the conditions, and outputting confidence frames.
The output quantity of the prior boxes is controlled by calculating the IOU to screen the boxes meeting the condition, and then the output quantity of the prior boxes is controlled by non-maximum suppression, the boxes with the confidence degree higher than a preset confidence threshold value are output, for example, a confidence threshold value a is set, for example, a is equal to 0.7, and the boxes with the confidence degree higher than a are output. The IOU is the intersection ratio of two frames, namely the area ratio of the frame generated by the model to the real frame, and the larger the IOU value is, the better the prediction effect of the generated frame is.
And step S104, inputting the confidence picture frame into a convolutional neural network for deep learning to obtain a fault type judgment result.
Adding the least black edges into the zoomed picture through self-adaption through a frame output by the confidence threshold, and putting the picture into a convolutional neural network for deep learning; through multilayer processing, after initial low-level feature representation is gradually converted into high-level feature representation, a complex transition resistance image frame can be completed through a model, and the recognition accuracy is improved through carrying out convolutional neural network training by using the frame for many times.
The model pre-trained on the coco data set by yolov5x is used for transfer learning, so that the model can identify high-level features, the features are universal, and classification of the training model can be realized only by training a full connection layer, so that the detection of the fault waveform type is completed.
Of course, according to the teaching of the embodiment of the application, other deep learning algorithms, computer vision, instance segmentation models, frames and the like can be adopted to analyze and judge the line fault type, so that the stable operation of the line is effectively ensured.
In one embodiment, the step of determining the fault type of the line according to the fault oscillogram includes:
step S201, using image recognition algorithm, according to transition resistance RFMatching the drawn fault waveform image with a defined fault waveform image in a fault history library, and judging the fault type of the line; image recognition algorithm and method based on transition resistance RFThe step of drawing the fault waveform diagram has been explained in the above embodiments, and is not described herein again;
step S202, a step of verifying the fault type determination result, specifically:
according To a UO waveform diagram in the fault time, calculating the frequency of a jumping period To of the UO waveform, and judging the fault type according To the frequency of the jumping period To;
if the results are the same, returning the judged fault type;
if the results are different, calling a dynamic thematic map of the longitude and latitude position of the fault point, and returning the fault type consistent with the judgment result of the dynamic thematic map based on the judgment result of the dynamic thematic map; if the dynamic thematic map is not changed, returning the judgment result of the original fault type.
For example, if the fault is caused by bird damage or windage yaw, the checking method in step S202 is adopted to check the judgment result of the fault type, and if the results are the same, the judged fault type is returned; if the results are different, calling a bird and wind dynamic thematic map (the thematic map is the existing data and only needs to be inquired), inquiring the bird and wind dynamic thematic map at the longitude and latitude position of the fault point, and returning the fault type consistent with the thematic map judgment result based on the judgment result of the dynamic thematic map; if the dynamic thematic map is not changed, returning the judgment result of the original fault type.
By analyzing and judging the method, the type judgment and verification of the fault oscillogram are realized, and the accuracy of fault judgment is ensured.
In another embodiment, as shown in fig. 2, there is provided a fault type discrimination system based on a fault waveform, including:
the oscillogram drawing module 301 is configured to obtain a fault oscillogram according to the fault recording data;
and a fault type determining module 302, configured to determine a fault type of the line according to the fault oscillogram.
The waveform drawing module 301 is specifically configured to:
acquiring the starting time and the ending time of the fault according to the fault recording data;
and obtaining a fault oscillogram according to the starting time and the ending time of the fault.
The fault type determining module 302 is specifically configured to:
and matching the fault waveform image with a defined fault waveform image in a fault history library by using an image recognition algorithm, and judging the fault type of the line.
The image recognition algorithm comprises:
dividing the fault oscillogram into small areas;
generating a prior frame through a target detection algorithm;
screening the prior frames meeting the conditions, and outputting confidence frame;
and inputting the confidence picture frame into a convolutional neural network for deep learning to obtain a fault type judgment result.
The system also comprises a checking module for checking the fault type judgment result output by the fault type judgment module, and the checking module comprises:
according To a UO waveform diagram in the fault time, calculating the frequency of a jumping period To of the UO waveform, and judging the fault type according To the frequency of the jumping period To;
if the results are the same, returning the judged fault type;
if the results are different, calling a dynamic thematic map of the longitude and latitude position of the fault point, and returning the fault type consistent with the judgment result of the dynamic thematic map based on the judgment result of the dynamic thematic map; if the dynamic thematic map is not changed, returning the judgment result of the original fault type.
The working principle of the fault type discrimination system based on the fault waveform in the embodiment of the present application is the same as that of the fault type discrimination method based on the fault waveform in each of the embodiments described above, and details are not repeated here.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program is executed by a processor to carry out the steps in the above-described method embodiments.
Those skilled in the art will appreciate that the configuration shown in fig. 3 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing devices to which aspects of the present invention may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The present invention is not limited to the structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (16)

1.一种基于故障波形的故障类型判别方法，其特征在于，包括以下步骤：1. a fault type discrimination method based on fault waveform, is characterized in that, comprises the following steps: 根据故障录波数据，获得故障波形图；Obtain the fault waveform diagram according to the fault recording data; 根据故障波形图，判断线路的故障类型。According to the fault waveform diagram, determine the fault type of the line. 2.如权利要求1所述的基于故障波形的故障类型判别方法，其特征在于，2. the fault type discrimination method based on fault waveform as claimed in claim 1 is characterized in that, 所述根据故障录波数据，获得故障波形图的步骤，包括：The step of obtaining the fault waveform diagram according to the fault recording data includes: 步骤(A)，根据故障录波数据，获得故障起始时刻和终止时刻；Step (A), according to the fault recording data, obtain the fault start time and the end time; 步骤(B)，根据故障起始时刻和终止时刻，获得故障波形图。In step (B), the fault waveform diagram is obtained according to the fault starting time and the ending time. 3.如权利要求2所述的基于故障波形的故障类型判别方法，其特征在于，3. The fault type discrimination method based on fault waveform as claimed in claim 2, is characterized in that, 所述步骤(A)中，根据故障录波数据，获得故障起始时刻的步骤，包括：In the described step (A), according to the fault recording data, the step of obtaining the fault starting time includes: 当电流启动的突变量满足启动判据时，判断为故障起始时刻，判据公式如下：When the sudden change of current startup meets the startup criterion, it is judged as the starting time of the fault, and the criterion formula is as follows: |Δi(n)|＝|i(n)-i(n-N)|＞ΔIs.set (式1)|Δi(n)|=|i(n)-i(nN)|>ΔI s.set (Equation 1) 式中，ΔIs.set表示突变量启动判断的整定值，ΔIs.set>0；In the formula, ΔI s.set represents the setting value of the mutation start judgment, ΔI s.set >0; N代表每周波采样次数；N represents the number of sampling times per cycle; n代表时间节点，i(n)代表时间节点的电流值；n represents the time node, and i(n) represents the current value of the time node;
Figure FDA0003393378010000011
Figure FDA0003393378010000011
 式中，kd表示突变量启动判据整定值系数；In the formula, k d represents the setting value coefficient of the mutation start criterion; IA0表示A相电流稳态幅值；I A0 represents the steady-state amplitude of phase A current; IB0表示B相电流稳态幅值；I B0 represents the steady-state amplitude of phase B current; IC0表示C相电流稳态幅值。I C0 represents the steady-state amplitude of the C-phase current. 4.如权利要求2所述的基于故障波形的故障类型判别方法，其特征在于，4. The fault type discrimination method based on fault waveform as claimed in claim 2, is characterized in that, 所述步骤(A)中，根据故障录波数据，获得故障终止时刻的步骤，包括：In the described step (A), according to the fault recording data, the step of obtaining the fault termination time includes: 若一个周期中，该周期的各采样点同时满足一周期内故障分量的绝对值均小于负荷电流幅值的一半，一周期内故障相的电流幅值均小于负荷电流幅值，该周期起始时刻往前推一周期，即为故障终止时刻。In a cycle, if the absolute value of the fault component in one cycle is less than half of the load current amplitude, and the current amplitude of the fault phase in one cycle is less than the load current amplitude, the cycle starts The time is pushed forward by one cycle, which is the failure termination time. 5.如权利要求2所述的基于故障波形的故障类型判别方法，其特征在于，5. The fault type discrimination method based on fault waveform as claimed in claim 2, is characterized in that, 所述步骤(B)，根据故障起始时刻和终止时刻，获得故障波形图，包括：The step (B), according to the fault start time and the end time, obtain the fault waveform diagram, including: 根据故障起始时刻和终止时刻，获得在故障发生时间内的ABC三相电压和波形图。According to the starting time and ending time of the fault, the ABC three-phase voltage and waveform diagrams during the fault occurrence time are obtained. 6.如权利要求5所述的基于故障波形的故障类型判别方法，其特征在于，6. The fault type discrimination method based on fault waveform as claimed in claim 5, is characterized in that, 所述根据故障波形图，判断线路的故障类型，包括：According to the fault waveform diagram, the fault type of the line is judged, including: 根据故障发生时间内的ABC三相电压和波形图，计算固定时间内ABC三相电压和波形的跳动周期的次数，根据跳动周期的次数判断故障类型。According to the ABC three-phase voltage and waveform diagram during the fault occurrence time, calculate the number of beat cycles of the ABC three-phase voltage and waveform within a fixed time, and judge the fault type according to the number of beat cycles. 7.如权利要求2所述的基于故障波形的故障类型判别方法，其特征在于，7. The fault type discrimination method based on fault waveform as claimed in claim 2, is characterized in that, 所述步骤(B)，根据故障起始时刻和终止时刻，获得故障波形图，包括：The step (B), according to the fault start time and the end time, obtain the fault waveform diagram, including: 利用集中参数法计算故障距离：Calculate the fault distance using the lumped parameter method:
Figure FDA0003393378010000021
Figure FDA0003393378010000021
Figure FDA0003393378010000022
Figure FDA0003393378010000022
 式中，DmF为故障距离；F为故障点；m、n分别为两端母线位置；J为序网号，由故障类型决定，对三相短路J＝1；对两相短路，J＝1,2；接地短路，J＝1,2,0；ZJ代表阻抗；DL代表线路总长度；DnF代表n到F的距离；
Figure FDA0003393378010000029
表示故障点F的各序电压；
Figure FDA0003393378010000023
表示M端测量到的电压；
Figure FDA0003393378010000024
表示N端测量到的电压；
Figure FDA0003393378010000025
表示M端测量到的电流；
Figure FDA0003393378010000026
表示N端测量到的电流；In the formula, D mF is the fault distance; F is the fault point; m and n are the busbar positions at both ends; J is the serial number, which is determined by the fault type. For three-phase short-circuit, J=1; for two-phase short-circuit, J= 1,2; short circuit to ground, J=1,2,0; Z J represents impedance; D L represents the total length of the line; D nF represents the distance from n to F;
Figure FDA0003393378010000029
Represents the sequence voltages at fault point F;
Figure FDA0003393378010000023
Indicates the voltage measured at the M terminal;
Figure FDA0003393378010000024
Indicates the voltage measured at the N terminal;
Figure FDA0003393378010000025
Indicates the current measured at the M terminal;
Figure FDA0003393378010000026
Indicates the current measured at the N terminal; 联立式2、式3消除
Figure FDA0003393378010000027
得：Simultaneous type 2, type 3 elimination
Figure FDA0003393378010000027
have to:
Figure FDA0003393378010000028
Figure FDA0003393378010000028
 获得DmF后，联合式(2)求得的故障点F的各序电压UFJ，通过下式计算得出故障点处的短路电流各序分量；After obtaining D mF , combine the sequence voltages U FJ of the fault point F obtained by formula (2), and calculate the sequence components of the short-circuit current at the fault point by the following formula;
Figure FDA0003393378010000031
Figure FDA0003393378010000031
 获得过渡电阻RF，如下式:Obtain the transition resistance R F , as follows:
Figure FDA0003393378010000032
Figure FDA0003393378010000032
 式中，
Figure FDA0003393378010000033
为故障点电流，
Figure FDA0003393378010000034
为故障点电压；In the formula,
Figure FDA0003393378010000033
is the fault point current,
Figure FDA0003393378010000034
is the fault point voltage; 根据过渡电阻RF绘制故障发生时间的故障波形图。 Draw the fault waveform diagram of the fault occurrence time according to the transition resistance RF. 8.如权利要求7所述的基于故障波形的故障类型判别方法，其特征在于，8. The fault type discrimination method based on fault waveform as claimed in claim 7, is characterized in that, 所述根据故障波形图，判断线路的故障类型，包括：According to the fault waveform diagram, the fault type of the line is judged, including: 利用图像识别算法，将根据过渡电阻RF绘制的故障波形图与故障历史库中已定义好的故障波形图像进行匹配，判断线路的故障类型。Using the image recognition algorithm, the fault waveform diagram drawn according to the transition resistance RF is matched with the fault waveform image that has been defined in the fault history database, and the fault type of the line is judged. 9.如权利要求8所述的基于故障波形的故障类型判别方法，其特征在于，9. The fault type discrimination method based on fault waveform as claimed in claim 8, is characterized in that, 所述图像识别算法包括：The image recognition algorithm includes: 将故障波形图分割成小块区域；Divide the fault waveform into small areas; 通过目标检测算法生成先验框；Generate a priori frame by target detection algorithm; 筛选符合条件的先验框，输出置信图框；Filter the a priori boxes that meet the conditions, and output the confidence picture box; 将置信图框输入卷积神经网络进行深度学习，获得故障类型判断结果。Input the confidence frame into the convolutional neural network for deep learning to obtain the fault type judgment result. 10.如权利要求8或9所述的基于故障波形的故障类型判别方法，其特征在于，所述方法还包括对故障类型判断结果进行校验的步骤，具体为：10. The fault type discrimination method based on fault waveform as claimed in claim 8 or 9, wherein the method further comprises the step of verifying the fault type judgment result, specifically: 根据故障发生时间内的ABC三相电压和波形图，计算固定时间内ABC三相电压和波形的跳动周期的次数，根据跳动周期的次数判断故障类型；According to the ABC three-phase voltage and waveform diagram in the time of fault occurrence, calculate the number of beating cycles of ABC three-phase voltage and waveform within a fixed time, and judge the fault type according to the number of beating cycles; 如果结果相同，则返回已判断出的故障类型；If the result is the same, return the fault type that has been judged; 如果结果不同，则调取故障点所处的经纬度位置的动态专题图，以动态专题图的判断结果为准，返回与动态专题图判断结果一致的故障类型；如果动态专题图未变化，则返回原故障类型判断结果。If the results are different, the dynamic thematic map of the latitude and longitude location of the fault point is retrieved, and the judgment result of the dynamic thematic map shall prevail, and the fault type that is consistent with the judgment result of the dynamic thematic map shall be returned; if the dynamic thematic map has not changed, return The judgment result of the original fault type. 11.一种基于故障波形的故障类型判别系统，其特征在于，包括：11. A fault type discrimination system based on fault waveform is characterized in that, comprising: 波形图绘制模块，用于根据故障录波数据，获得故障波形图；The waveform drawing module is used to obtain the fault waveform according to the fault recording data; 故障类型判断模块，用于根据故障波形图，判断线路的故障类型。The fault type judgment module is used to judge the fault type of the line according to the fault waveform diagram. 12.如权利要求11所述的基于故障波形的故障类型判别系统，其特征在于，12. The fault type discrimination system based on fault waveform according to claim 11, wherein, 所述波形图绘制模块，具体用于：The waveform drawing module is specifically used for: 根据故障录波数据，获得故障起始时刻和终止时刻；According to the fault recording data, obtain the fault start time and end time; 根据故障起始时刻和终止时刻，获得故障波形图。According to the fault start time and end time, the fault waveform diagram is obtained. 13.如权利要求11所述的基于故障波形的故障类型判别方法，其特征在于，13. The fault type discrimination method based on fault waveform according to claim 11, wherein, 所述故障类型判断模块，具体用于：The fault type judgment module is specifically used for: 利用图像识别算法，将根据故障波形图与故障历史库中已定义好的故障波形图像进行匹配，判断线路的故障类型。Using the image recognition algorithm, the fault waveform image will be matched with the fault waveform image defined in the fault history database to determine the fault type of the line. 14.如权利要求13所述的基于故障波形的故障类型判别系统，其特征在于，14. The fault type discrimination system based on fault waveform according to claim 13, wherein, 所述图像识别算法包括：The image recognition algorithm includes: 将故障波形图分割成小块区域；Divide the fault waveform into small areas; 通过目标检测算法生成先验框；Generate a priori frame by target detection algorithm; 筛选符合条件的先验框，输出置信图框；Filter the a priori boxes that meet the conditions, and output the confidence picture box; 将置信图框输入卷积神经网络进行深度学习，获得故障类型判断结果。Input the confidence frame into the convolutional neural network for deep learning to obtain the fault type judgment result. 15.如权利要求14所述的基于故障波形的故障类型判别方法，其特征在于，所述系统还包括校验模块，用于对故障类型判断模块输出的故障类型判断结果进行校验，包括：15. The fault type discrimination method based on fault waveform as claimed in claim 14, wherein the system further comprises a verification module for verifying the fault type determination result output by the fault type determination module, comprising: 根据故障发生时间内的ABC三相电压和波形图，计算固定时间内ABC三相电压和波形的跳动周期的次数，根据跳动周期的次数判断故障类型；According to the ABC three-phase voltage and waveform diagram in the time of fault occurrence, calculate the number of beating cycles of ABC three-phase voltage and waveform within a fixed time, and judge the fault type according to the number of beating cycles; 如果结果相同，则返回已判断出的故障类型；If the result is the same, return the fault type that has been judged; 如果结果不同，则调取故障点所处的经纬度位置的动态专题图，以动态专题图的判断结果为准，返回与动态专题图判断结果一致的故障类型；如果动态专题图未变化，则返回原故障类型判断结果。If the results are different, the dynamic thematic map of the latitude and longitude location of the fault point is retrieved, and the judgment result of the dynamic thematic map shall prevail, and the fault type that is consistent with the judgment result of the dynamic thematic map shall be returned; if the dynamic thematic map has not changed, return The judgment result of the original fault type. 16.一种计算机设备，其特征在于，所述计算机设备包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现如权利要求1至10任一项所述方法的步骤。16. A computer device, characterized in that the computer device comprises a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the computer program as claimed in any one of claims 1 to 10 is implemented. steps of the method described.
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CN115311542A (en) * 2022-08-25 2022-11-08 杭州恒胜电子科技有限公司 Target detection method, device, equipment and medium
CN115523608A (en) * 2022-08-25 2022-12-27 青岛海尔空调器有限总公司 Fault detection method and device for air conditioner, air conditioner, storage medium
CN117216485A (en) * 2023-11-09 2023-12-12 国网山东省电力公司电力科学研究院 Objective weighting-based power transmission wave-recording bird damage fault judging method and system
CN117216485B (en) * 2023-11-09 2024-01-30 国网山东省电力公司电力科学研究院 Objective weighting-based power transmission wave-recording bird damage fault judging method and system
CN117554809A (en) * 2024-01-10 2024-02-13 北京博华鑫诺科技有限公司 Motor winding balance detection method and device, electronic equipment and storage medium
CN117554809B (en) * 2024-01-10 2024-03-22 北京博华鑫诺科技有限公司 Motor winding balance detection method and device, electronic equipment and storage medium

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