KR101729112B1 - Method and device for diagnosing Partial Discharge of Gas Insulated Switchgear - Google Patents

Abstract

The present invention relates to a method and an apparatus for diagnosing the partial discharge of a gas insulated switchgear to analyze the frequency and phase characteristic of a partial discharge signal and determine the cause of the partial discharge fault of the gas insulated switchgear. The method for diagnosing the partial discharge of a gas insulated switchgear according to the present invention includes a partial discharge signal detecting step of detecting a partial discharge signal, a partial discharge signal analysis step of analyzing at least one of a predetermined frequency and a phase characteristic with respect to the partial discharge signal without phase voltage and phase synchronization, and a partial discharge defect cause diagnosis step of diagnosing the cause of the partial discharge defect based on the frequency and phase characteristic of the partial discharge signal analyzed in the partial discharge signal analysis step.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a method and a device for diagnosing a partial discharge of a gas insulated switchgear,

The present invention relates to a method and an apparatus for diagnosing a partial discharge of a gas insulated switchgear, and more particularly, to analyzing a partial discharge signal to determine a cause of a defect in a partial discharge of a gas insulated switchgear. That is, the present invention relates to a method and an apparatus for partial discharge diagnosis of a gas insulated switchgear which can analyze the frequency and phase characteristics of a partial discharge signal to determine the cause of a partial discharge fault of the gas insulated switchgear.

Gas Insulated Switchgear refers to a complex switchgear insulated with insulation gas which safely opens and closes the line in an abnormal state such as an accident or a short circuit as well as a normal state opening and closing in a substation.

The gas insulated switchgear is constructed in such a manner that a switching device such as a disconnector (DS), a grounding switch (ES), a breaker (CB), and a bus are housed in a metallic enclosure filled with insulating gas.

In recent years, ultra-high frequency (UHF) partial discharge detection sensors have been applied to a variety of gas insulated switchgear as a diagnostic tool for gas insulated switchgear.

Typically, the gas insulated switchgear comprises spacers for connecting the cylindrical metal enclosures at proper intervals, and is made of an insulating material such as epoxy. When a partial discharge is generated in the gas insulated switchgear, a very high frequency (UHF) By using a leak point, a partial discharge detection sensor is attached to the outside of the spacer, the electromagnetic wave generated inside the gas insulated switchgear is sensed, and the noise mixed with the sensing signal is removed to diagnose the abnormality such as the degree of deterioration, And there has been much research on a diagnostic apparatus or method for such a gas insulated switchgear.

For example, Korean Patent Publication No. 10-2009-0075657 discloses a partial discharge detection sensor for detecting a partial discharge when a partial discharge is generated due to an abnormal defect in the GIS, a noise detection sensor for detecting noise, And a synchronizing device for outputting the data synchronously with the frequency of the phase voltage.

However, even in this case, since the partial discharge signal can be diagnosed only after synchronizing with the phase voltage, there is a disadvantage that it can not prevent the danger that may occur in the process of synchronizing with the phase voltage.

Korean Patent Publication No. 10-2009-0075657 (2009.07.08)

An object of the present invention is to provide a method and an apparatus for partial discharge diagnosis of a gas insulated switchgear which can diagnose a cause of a partial discharge defect by analyzing the frequency and phase characteristics of a partial discharge signal without phase synchronization with phase voltage.

The present invention provides a partial discharge diagnosis method and apparatus for a partial discharge diagnosis of a gas insulated switchgear which can diagnose a cause of a partial discharge defect without phase synchronization with a phase voltage, .

A partial discharge diagnosis method for a gas insulated switchgear according to the present invention includes a partial discharge signal detection step for detecting a partial discharge signal, a partial discharge for analyzing at least one of predetermined frequency and phase characteristics for a partial discharge signal without phase voltage and phase synchronization A signal analysis step, and a partial discharge defect analysis step of diagnosing a cause of a partial discharge defect based on frequency and phase characteristics of the partial discharge signal analyzed in the partial discharge signal analysis step.

Here, the partial discharge signal analysis step includes a frequency distribution discrimination step for discriminating a frequency distribution including a partial discharge signal, a maximum value measurement step for discriminating a maximum size among the partial discharge signals, a first maximum- A maximum frequency discrimination step of discriminating frequency distributions of the maximum size frequency, a maximum frequency discrimination step of discriminating the maximum difference between the maximum sizes of the frequency distributions including the partial discharge signals, And a density discrimination step for discriminating the density of the partial discharge signal according to the frequency distribution including the signal.

In addition, the frequency distribution distinguishing step can distinguish the frequency distribution by at least one.

Here, the maximum value measuring step may measure the maximum magnitude value of the partial discharge signal.

Also, the frequency distribution discrimination step of the maximum magnitude frequency can distinguish the frequency distribution including the first largest magnitude frequency having the largest magnitude and the second magnitude frequency having the next largest magnitude.

Here, the step of discriminating the maximum difference of the maximum size frequencies according to the frequency distribution can distinguish the difference between the largest size and the smallest size among the maximum size frequencies according to the frequency distribution.

The density discrimination step for each frequency distribution can distinguish the density based on the number of detected signals for each frequency distribution.

Here, the partial discharge signal analysis step may include a phase grouping step for classifying the number of phase clusters with respect to a maximum magnitude frequency of the partial discharge signal, a phase total range for distinguishing a phase range in which phases are distributed with respect to a maximum- A phase discrimination step for discriminating the phase ranges distributed by the phase clusters with respect to the maximum magnitude frequency of the partial discharge signal, a density classification step for discriminating signal densities of the phase clusters with respect to the maximum magnitude frequency of the partial discharge signal, A step of measuring a maximum size difference of each phase cluster for measuring a difference between a maximum size of the phase clusters with respect to a maximum size frequency of the partial discharge signal and a phase cluster communicating the shape of the phase cluster with respect to a maximum size frequency of the partial discharge signal And a shape distinguishing step.

In addition, the step of distinguishing the number of phase clusters distinguishes the number of clusters when the clusters are clearly classified, and the noise when the number of clusters is 3 or more.

Here, the phase full range distinguishing step can distinguish whether or not the phase range occurs over the full phase.

In addition, the step of distinguishing ranges by phase clusters can distinguish at least one of whether or not the phase range exceeds the 90 degree reference and whether there is a phase cluster that can be distinguished.

Here, the density classification step for each phase cluster can distinguish the density based on the number of signals per phase cluster.

Also, the step of measuring the maximum size difference according to the phase clusters can distinguish the largest size and the smallest size among the maximum size phases of each phase cluster.

Here, the phase cluster type distinguishing step may distinguish one of the similar cluster number, the cluster unobscured, and the non-cluster by distinguishing similarity with at least one of the predetermined one or more clusters.

Also, the cause of the partial discharge defect may be a part including at least any one of floating, void, TR fault, protrusion, crack, and free particle. A cause of a discharge defect, or a cause of a noise defect including at least one of an external corona, an external noise, a sensor connector fault, and a mobile phone signal.

Also, the gas insulated switchgear partial discharge diagnosis apparatus includes a partial discharge signal detecting unit for detecting a partial discharge signal, a partial discharge signal analyzing unit for analyzing at least any one of a predetermined frequency and a phase characteristic with respect to the partial discharge signal, And a partial discharge defect cause diagnosis unit for diagnosing a cause of the partial discharge defect based on the predetermined frequency and phase characteristics of the partial discharge signal analyzed by the partial discharge signal analysis unit.

Here, the partial discharge signal analysis unit

For frequency analysis of the partial discharge signal, the frequency distribution including the partial discharge signal, the maximum size among the partial discharge signals, the frequency distribution of the first maximum size frequency and the second maximum size frequency of the partial discharge signal, It is possible to distinguish the maximum difference of the maximum size for each frequency distribution and the density of the partial discharge signal for each frequency distribution including the partial discharge signal.

Also, the partial discharge signal analysis unit

For the phase analysis of the partial discharge signal, the number of phase clusters, the phase range distributed by the phase clusters, the phase range distributed by the phase clusters, the signal density by the phase clusters, Differences, and shapes of the phase clusters.

The method and apparatus for diagnosing a partial discharge of a gas insulated switchgear according to the present invention have an advantage of analyzing a partial discharge signal to determine the cause of a partial discharge of a gas insulated switchgear.

The method and apparatus for partial discharge diagnosis of a gas insulated switchgear according to the present invention analyzes the frequency and phase characteristics of a partial discharge signal to determine the cause of a partial discharge defect of the gas insulated switchgear and diagnose the cause of a partial discharge defect So that it is possible to prevent the danger that may occur in the process of synchronizing with the phase voltage.

FIG. 1 is a flowchart showing a partial discharge diagnosis method of a gas insulated switchgear according to an embodiment of the present invention.
FIG. 2 is a flowchart showing the frequency characteristic analysis in detail during the partial discharge signal analysis step of FIG. 1; FIG.
3 is a table used for analysis of frequency characteristics during the partial discharge signal analysis step of FIG.
FIG. 4 is a frequency spectrum showing an example of analyzing the frequency characteristic in the partial discharge signal analysis step of FIG. 1 in detail.
FIG. 5 is a flowchart showing the phase characteristic analysis in detail in the partial discharge signal analysis step of FIG. 1; FIG.
FIG. 6 is a table used for phase characteristic analysis in the partial discharge signal analysis step of FIG.
FIG. 7 is a phase spectrum showing an example of analyzing the phase characteristic in the partial discharge signal analysis step of FIG. 1 in detail.
8 is a flowchart showing the entire flow of FIG. 1 in detail.
FIG. 9 is a flowchart showing an example of diagnosing a cause of a partial discharge defect in the step of diagnosing a cause of the partial discharge defect in FIG.
10 is a table showing an example of a defect cause of the partial discharge determined in the step of diagnosing the cause of the partial discharge defect in FIG.
11 is a block diagram illustrating a gas insulated switchgear partial discharge diagnosis apparatus according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It is to be understood that the present invention is not intended to be limited to the specific embodiments but includes all changes, equivalents, and alternatives included in the spirit and scope of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings.

FIG. 1 is a flowchart illustrating a partial discharge diagnosis method of a gas insulated switchgear according to an embodiment of the present invention, and FIGS. 2 to 10 are a flowchart, a table, and a spectrum for explaining FIG. 1 in detail.

Hereinafter, a partial discharge diagnosis method for a gas insulated switchgear according to an embodiment of the present invention will be described with reference to FIGS. 1 to 10. FIG.

Referring to FIG. 1, a method for diagnosing a partial discharge of a gas insulated switch according to an exemplary embodiment of the present invention includes a step S100 of detecting a partial discharge signal, a step S100 of detecting a partial discharge signal, (S300) of diagnosing the cause of the partial discharge based on the frequency and phase characteristics of the partial discharge signal analyzed in the partial discharge signal analysis step (S200) and analyzing at least one of the phase characteristics do.

Also, the cause of the partial discharge defect may be a part including at least any one of floating, void, TR fault, protrusion, crack, and free particle. A cause of a discharge defect or a cause of a noise defect including at least one of an external corona, an external noise, a sensor connector fault, and a mobile phone signal.

In detail, in the partial discharge signal detection step S100, the partial discharge signal is detected from the gas insulated switch, and in the partial discharge signal analysis step S200, the frequency and phase characteristics of the partial discharge signal are analyzed without phase synchronization of the phase voltage. There is an effect of preventing the danger that may occur in the process of synchronizing with the voltage.

In addition, frequency and phase characteristics are input sequentially in the cause of partial discharge defect diagnosis (S300), so that the cause of partial discharge defects such as partial discharge and noise can be easily determined.

Hereinafter, FIGS. 2 to 4 illustrate a method for analyzing the frequency characteristics of the partial discharge signal, and FIGS. 5 to 7 illustrate a method for analyzing the phase characteristics of the partial discharge signal. 8 to 10 illustrate a method of determining the cause of a defect of a partial discharge signal based on frequency and phase characteristics of a partial discharge signal.

FIG. 2 is a flowchart showing the frequency characteristic analysis in detail in the partial discharge signal analysis step (S200) of FIG.

2, the partial discharge signal analysis step S200 includes a step S211 of discriminating a frequency distribution including a partial discharge signal, a step S212 of discriminating a maximum size among the partial discharge signals, A step S213 of discriminating a frequency distribution of the first maximum magnitude frequency of the signal and a frequency distribution of the second maximum magnitude frequency, a step S214 of discriminating the maximum difference of the maximum magnitudes of the frequency distributions including the partial discharge signal, And a step S215 of discriminating the density of the partial discharge signal for each frequency distribution.

In addition, the frequency distribution distinguishing step S211 distinguishes the frequency distribution by at least one.

Here, the maximum value measurement step S212 measures the maximum magnitude value of the partial discharge signal.

In addition, the frequency distribution discrimination step S213 of the maximum magnitude frequency distinguishes the frequency distribution including the first largest magnitude frequency having the largest magnitude and the second magnitude frequency having the next largest magnitude.

Here, the maximum difference discrimination step S214 of the maximum magnitude frequency for each frequency distribution distinguishes between the largest magnitude and the smallest magnitude of the maximum magnitude frequency for each frequency distribution.

In addition, the frequency distribution-based density discrimination step (S215) distinguishes the density based on the number of detection signals for each frequency distribution.

For example, in the frequency distribution distinguishing step S211, the frequency distribution is divided into F1, F2, and F3 to indicate the frequency distribution including the partial discharge signal, whether or not the frequency distribution is included in all frequencies, The maximum size can be measured.

On the other hand, in the frequency distribution discrimination step S213 of the maximum magnitude frequency, it is discriminated whether the first maximum magnitude frequency and the second maximum magnitude frequency are in the frequency distributions F1, F2, and F3, In step S214, the maximum difference in F1, the maximum size in F2, and the maximum size in F3 are distinguished from each other.

In step S215, the frequency discrimination step S215 discriminates whether there are several partial discharge signals for each of F1, F2, and F3, and discriminates high, medium, low, and none.

FIG. 3 is a table used for frequency characteristic analysis during the partial discharge signal analysis step (S200) of FIG. 3, the frequency characteristic of the partial discharge signal is divided into a 'distribution' for discriminating in the frequency distribution distinguishing step S211, a 'distribution' for discriminating in the maximum value measuring step S212 a maximum magnitude distribution distinguishable in the frequency distribution discrimination step S213 of the maximum magnitude frequency and a peak magnitude distribution in the maximum magnitude frequency discrimination step S214 according to the frequency distribution, (Density), which is distinguished in the frequency distribution-based density discrimination step S215.

In detail, 'distribution' distinguishes between all frequency ranges or specific ranges, 'maximum magnitude' is the maximum magnitude value, 'maximum magnitude distribution' The difference between the first maximum magnitude value and the second largest magnitude frequency difference is discriminated, and the 'Peak Difference' indicates that the difference between the first maximum magnitude value and the second maximum magnitude value is larger or smaller And 'Density' can distinguish whether the number of signals included in the frequency distribution is high, medium, low, or not.

FIG. 4 is a frequency spectrum showing an example of analyzing the frequency characteristic in the partial discharge signal analysis step (S200) of FIG. 1 in detail. As shown in FIG. 4, according to the classification method of the density distribution step S215 for each frequency distribution, (a) is a case of high density, (b) is a case where the density is intermediate, (D) shows the case where there is no density.

FIG. 5 is a flowchart illustrating the phase characteristic analysis of the partial discharge signal analysis step S200 of FIG. 1 in more detail.

As shown in FIG. 5, the partial discharge signal analysis step S200 includes a step S221 of discriminating the number of phase clusters with respect to the maximum magnitude frequency of the partial discharge signal, (S222) of discriminating phase ranges distributed by phase clusters with respect to the maximum magnitude frequency of the partial discharge signal, determining a signal density per phase cluster for the maximum magnitude frequency of the partial discharge signal A step S224 of determining a maximum magnitude of the partial discharge signal, a step S225 of measuring a difference between the maximum magnitudes of the phase clusters with respect to the maximum magnitude frequency of the partial discharge signal, and a step of discriminating the shape of the phase cluster with respect to the maximum magnitude frequency of the partial discharge signal S226).

In addition, the phase clustering step S221 distinguishes the number of clusters when the clusters are clearly classified and the noise when the number of clusters is three or more.

Here, the phase total range distinguishing step S222 discriminates whether or not the phase range occurs over the full phase.

In addition, the step S223 of distinguishing ranges for each phase cluster distinguishes at least one of whether or not the phase range exceeds the 90 degree reference and whether there is a phase cluster that can be distinguished.

Here, the density classification step (S224) for each phase cluster distinguishes the density based on the number of signals per phase cluster.

In addition, the step of measuring the maximum size difference according to the phase cluster (S225) distinguishes between the largest size and the smallest size among the maximum size phases of the phase clusters.

Here, the phase cluster shape distinguishing step (S226) distinguishes between similar cluster number, cluster unambiguous, and no cluster by distinguishing similarity with at least one or more predetermined ones.

For example, in the phase cluster counting step S221, when the number of phase clusters is discriminated to represent one or both of the number of phase clusters, the phase clusters are expressed as one, and when two phase clusters are detected in the first cluster and the second cluster If the number of phase clusters is three or more, it can be distinguished by noise.

In the phase total range distinguishing step S222, it is judged whether or not a phase for the maximum magnitude frequency of the partial discharge has occurred for the full phase range. In step S223, it is possible to distinguish whether the first cluster and the second cluster exceed 90 degrees and the case where there is no phase cluster. In the density classification step S224, the first and second clusters can be classified into high, medium, low, and none based on the detected number of phase patterns.

In step S225, it is possible to determine whether there is a large or small peak difference between the first cluster and the second cluster. In step S226, And the number of the cluster is compared with the stored cluster shape to distinguish the numbers of the cluster shapes having similar shapes, or the case where the cluster is unclear or the cluster having no phase cluster can be distinguished.

FIG. 6 is a table used for phase characteristic analysis in the partial discharge signal analysis step (S200) of FIG. 6, the phase characteristic for the partial discharge signal is divided into a 'group distribution' for discriminating in the step S221 of distinguishing the phase crowds, 'a group distribution' for discriminating in the phase total range distinguishing step S222, A 'partial range' distinguishing in the phase cluster sorting step S223, a 'density' discriminating in the density discriminating step S224 for each phase cluster, Can be distinguished by a 'Magnitude' distinguishing in the maximum size difference measurement step S225 and a 'Shape' distinguishing in the phase cluster shape discrimination step S226.

In detail, 'group distribution' distinguishes the number of clusters in which the phases are distributed, 'full range' distinguishes whether the separated clusters are distributed over the whole phase, and 'partial range partial range 'is divided into 90 degrees or less or less or less, depending on the cluster. In this case, 90 degrees is only an example, and it can be changed to 180 or 270 depending on the case.

The 'Density' distinguishes the first cluster and the second cluster as high, medium, low, or none depending on the number occupied by the signal. The 'magnitude' And 'Shape' can distinguish the cluster shape number of each cluster or distinguish the case where the cluster is unclear or there is no phase cluster.

FIG. 7 is a detailed phase spectrum illustrating an example of analyzing the phase characteristic in the partial discharge signal analysis step (S200) of FIG. As can be seen from FIG. 7, the phase clusters classified in the phase cluster counting step S221 can be divided into two phases as shown in the phase groups 610 and 620, 610 and 620 do not occur in the full phase.

The phase group 610 and the phase group 620 are discriminated to have a phase occupation range exceeding 90 degrees through the step S223 of distinguishing ranges for each phase cluster and the phase group 610 is discriminated through the phase discrimination step S224 for each phase group. The density is high, and the density of 620 is distinguished from the density of the phase group 610 by the medium.

Meanwhile, the difference in the maximum size among the phase clusters may be similar through the step S225 of measuring the maximum size difference for each phase cluster.

8 is a flowchart showing the entire flow of FIG. 1 in detail. As can be seen from FIG. 8, in the partial discharge defect cause diagnosis step (S300), the analysis result of the partial discharge signal analysis step (S200) is input one by one to diagnose the cause of the partial discharge defect.

For example, in the partial discharge defect cause diagnosis step S300, the number of phase clusters in the step S221 of discriminating the number of phase clusters as the phase characteristic, the phase range in the phase total range distinguishing step S222, (S224), the maximum size difference in the phase cluster (S225) in measuring the maximum size difference according to the phase cluster, the phase cluster shape discrimination The shape of each phase cluster in step S226, and the like.

In addition, as the frequency characteristics, the frequency distribution in the frequency distribution discrimination step S211, the maximum size frequency magnitude in the maximum value measurement step S212, the frequency distribution of the maximum magnitude frequency in the frequency distribution discrimination step S213 of the maximum magnitude frequency , The maximum difference in the maximum magnitude frequency for each frequency distribution in the maximum difference discrimination step S214 of the maximum magnitude frequency for each frequency distribution, the density for each frequency distribution in the density discrimination step S215 for each frequency distribution, It is possible to automatically diagnose the cause of the defect.

9 is a flowchart showing an example of diagnosing a cause of a partial discharge defect in the partial discharge defect cause diagnosis step S300 of FIG. As can be seen from FIG. 9, according to the input of the phase cluster, the cluster shape is distinguished into two, and the cluster shape is discriminated by 1-1, the frequency distribution is in the whole frequency, and in the frequency distributions F1, F2, and F3, , It means that the cause of the defect of the partial discharge is judged as floating, outer corona, transformer defect (TR defect), sensor abnormality, void or the like.

10 is a table showing an example of a defect cause of the partial discharge determined in the partial discharge defect cause diagnosis step (S300) of FIG. As can be seen in FIG. 10, the cause of the partial discharge defect is floating, void, TR fault, conductor protrusion, crack, free particle A cause of a partial discharge defect including at least one of them, or a cause of a noise defect including at least one of an external corona, an external noise, a sensor connector fault, .

11 is a block diagram illustrating a gas insulated switchgear partial discharge diagnosis apparatus according to an embodiment of the present invention.

11, the gas insulated switchgear partial discharge diagnosis apparatus includes a partial discharge signal detector 100 for detecting a partial discharge signal, at least one of a predetermined frequency and phase characteristic for the partial discharge signal without phase synchronization with the phase voltage A partial discharge signal analyzer 200 for analyzing any one of the partial discharge signals and a partial discharge defect analyzing unit 200 for diagnosing a cause of a partial discharge defect based on predetermined frequency and phase characteristics of the partial discharge signal analyzed by the partial discharge signal analyzer 200, And a cause diagnosis unit (300).

Here, the partial discharge signal analysis unit 200

For frequency analysis of the partial discharge signal, the frequency distribution including the partial discharge signal, the maximum size among the partial discharge signals, the frequency distribution of the first maximum size frequency and the second maximum size frequency of the partial discharge signal, The maximum difference of the maximum size for each frequency distribution, and the density of the partial discharge signal for each frequency distribution including the partial discharge signal.

Also, the partial discharge signal analysis unit 200

For the phase analysis of the partial discharge signal, the number of phase clusters, the phase range distributed by the phase clusters, the phase range distributed by the phase clusters, the signal density by the phase clusters, Differences, and shapes of the phase clusters.

And the cause of a partial discharge defect including at least one of floating, void, TR fault, conductor protrusion, crack, free particle, a noise defect including at least one of an external corona, an external noise, a sensor connector fault, and a mobile phone signal.

The partial discharge signal analyzer 200 detects the partial discharge signal from the gas insulated switch in the partial discharge signal detector 100 and outputs the partial discharge signal to the partial discharge signal analyzer 200 without phase synchronization of the phase voltage. By analyzing the frequency and phase characteristics as described above, it is possible to diagnose the cause of the partial discharge defect or the cause of the noise defect, and the risk that may occur in the process of synchronizing with the phase voltage can be prevented. As described above, the method and apparatus for partial discharge diagnosis of a gas insulated switchgear according to the present invention can diagnose a cause of a partial discharge defect by analyzing the frequency and phase characteristics of a partial discharge signal without phase voltage and phase synchronization, It is possible to diagnose the cause of the defect of the partial discharge without phase synchronization and to prevent the danger that may occur in the process of synchronizing with the phase voltage.

What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe all possible combinations of components or methods for purposes of describing the embodiments described, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the described embodiments are intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims.

Claims (18)

delete A partial discharge signal detecting step of detecting a partial discharge signal;
A partial discharge signal analysis step of analyzing at least one of predetermined frequency and phase characteristics for the partial discharge signal without phase synchronization with the phase voltage; And
And a partial discharge defect cause diagnosis step of diagnosing a cause of the partial discharge defect based on the frequency and phase characteristics of the partial discharge signal analyzed in the partial discharge signal analysis step,
Wherein the partial discharge signal analysis step comprises:
A frequency distribution discrimination step of discriminating a frequency distribution including the partial discharge signal;
A maximum value measuring step of discriminating a maximum magnitude of a frequency peak value among the partial discharge signals;
Dividing a frequency distribution of a maximum magnitude frequency that distinguishes between a first maximum magnitude frequency of the partial discharge signal and a frequency distribution of a second maximum magnitude frequency;
A maximum difference discrimination step of discriminating a maximum difference of the maximum magnitudes of the frequency distributions including the partial discharge signal according to frequency distributions; And
And dividing the density of the partial discharge signal by frequency distribution including the partial discharge signal into frequency distributions. 3. The method of claim 2,
Wherein the frequency distribution distinguishing step distinguishes the frequency distribution by at least one. 3. The method of claim 2,
Wherein the maximum value measuring step measures a maximum magnitude value of the partial discharge signal. 3. The method of claim 2,
Wherein the step of discriminating the frequency distribution of the maximum magnitude frequency distinguishes the frequency distribution including the first maximum magnitude frequency having the largest magnitude and the second magnitude frequency having the next largest magnitude. Partial discharge diagnosis method. 3. The method of claim 2,
Wherein the step of distinguishing the maximum magnitude frequency among the frequency distributions distinguishes between the largest magnitude and the smallest magnitude between the maximum magnitude frequencies of the frequency distributions. 3. The method of claim 2,
Wherein the frequency distribution-based density discrimination step distinguishes the density based on the number of detection signals for each frequency distribution. A partial discharge signal detecting step of detecting a partial discharge signal;
A partial discharge signal analysis step of analyzing at least one of predetermined frequency and phase characteristics for the partial discharge signal without phase synchronization with the phase voltage; And
And a partial discharge defect cause diagnosis step of diagnosing a cause of the partial discharge defect based on the frequency and phase characteristics of the partial discharge signal analyzed in the partial discharge signal analysis step,
Wherein the partial discharge signal analysis step comprises:
Dividing the number of phase clusters into a maximum number of phase frequencies of the partial discharge signal;
A phase total range distinguishing step of discriminating a phase range in which phases are distributed with respect to a maximum magnitude frequency of the partial discharge signal;
A step of distinguishing a phase cluster by a phase cluster, the phase cluster being classified into a plurality of phases;
A density classifying step for classifying the signal density of each phase cluster with respect to a maximum magnitude frequency of the partial discharge signal;
Measuring a maximum size difference of each phase cluster for measuring a maximum size difference between phase clusters with respect to a maximum magnitude frequency of the partial discharge signal; And
And determining a shape of a phase cluster with respect to a maximum magnitude frequency of the partial discharge signal. 9. The method of claim 8,
Wherein the step of distinguishing the number of phase clusters distinguishes the number of clusters when the clusters are clearly distinguished and the noise when the number of clusters is three or more. 9. The method of claim 8,
Wherein said phase total range distinguishing step distinguishes whether or not said phase range occurs over an entire phase. 9. The method of claim 8,
Wherein the step of distinguishing ranges for each phase cluster distinguishes at least one of whether or not the phase range exceeds the 90 degree reference and the presence or absence of a phase cluster that can be distinguished. 9. The method of claim 8,
Wherein the step of distinguishing density for each phase cluster distinguishes the density based on the number of signals per phase cluster. 9. The method of claim 8,
Wherein the step of measuring the maximum size difference for each phase cluster distinguishes between the largest magnitude and the smallest magnitude of the maximum magnitude phase for each phase cluster. 9. The method of claim 8,
Wherein the phase cluster shape distinguishing step distinguishes between the similar cluster number, cluster unambiguous, and no cluster by distinguishing similarity with at least one of the predetermined one or more clusters. 9. The method according to claim 2 or 8,
The cause of the partial discharge defect may be a part including at least any one of floating, void, TR fault, protrusion, crack, free particle, A cause of a discharge defect or a cause of a noise defect including at least one of an external corona, an external noise, a sensor connector fault, and a mobile phone signal. Gas Insulated Switch Partial Discharge Diagnostic Method. delete A partial discharge signal detector for detecting a partial discharge signal;
A partial discharge signal analyzer for analyzing at least one of a predetermined frequency and a phase characteristic for the partial discharge signal without phase synchronization with the phase voltage; And
And a partial discharge defect cause diagnosis unit for diagnosing a cause of a partial discharge defect based on predetermined frequency and phase characteristics of the partial discharge signal analyzed by the partial discharge signal analysis unit,
Wherein the partial discharge signal analyzer comprises:
A frequency distribution including the partial discharge signal, a maximum magnitude of a frequency peak value of the partial discharge signal, a first maximum magnitude frequency of the partial discharge signal, A frequency distribution of a maximum size frequency, a maximum difference of a maximum size for each frequency distribution including the partial discharge signal, and a density of the partial discharge signal for each frequency distribution including the partial discharge signal. Partial discharge diagnostic apparatus. A partial discharge signal detector for detecting a partial discharge signal;
A partial discharge signal analyzer for analyzing at least one of a predetermined frequency and a phase characteristic for the partial discharge signal without phase synchronization with the phase voltage; And
And a partial discharge defect cause diagnosis unit for diagnosing a cause of a partial discharge defect based on predetermined frequency and phase characteristics of the partial discharge signal analyzed by the partial discharge signal analysis unit,
Wherein the partial discharge signal analyzer comprises:
For the phase analysis of the partial discharge signal, the number of phase clusters, the phase range in which phases are distributed, the phase range distributed by phase clusters, the signal density per phase cluster, The size difference, and the shape of the phase cluster.

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