KR102393662B1 - System and method for diagnosing partial discharge of electric power equipment, and a recording medium having computer readable program for executing the method - Google Patents

Abstract

Disclosed are a power device partial discharge analysis system and method, and a recording medium recording a computer readable program for executing the method. The power device partial discharge diagnosis system includes a signal measuring unit, a frequency analyzing unit, a peak extracting unit, a filtering unit, and a diagnosis unit. The signal measuring unit measures the partial discharge signal from the power device, the frequency analysis unit performs frequency analysis of the partial discharge signal, the peak extractor extracts a plurality of peaks in the order of magnitude in the frequency domain, and the filtering unit extracts a plurality of peaks The partial discharge signal is filtered for a frequency region corresponding to , and the diagnosis unit diagnoses the type of the partial discharge signal by comparing the signal characteristic of the filtered partial discharge signal with a preset reference signal characteristic.

Description

SYSTEM AND METHOD FOR DIAGNOSING PARTIAL DISCHARGE OF ELECTRIC POWER EQUIPMENT, AND A RECORDING MEDIUM HAVING COMPUTER READABLE PROGRAM FOR EXECUTING THE METHOD}

The present invention relates to a high voltage power device, and more particularly, to a system and method for diagnosing a partial discharge signal measured to monitor insulation deterioration in a high voltage power device such as a transformer or a circuit breaker.

Since the partial discharge signal is usually a very high frequency signal, a high-speed ADC is required to measure the original signal. This has disadvantages in terms of price and a long time required for signal analysis. Due to this problem, most of the existing partial discharge measurement systems use the Peak & Hold (or Level Detecting) method.

The Peak & Hold (or Level Detecting) method is a method of wave shaping a partial discharge signal, which is a very fast signal, as shown in FIG. 1, and then holding the peak to sample only the maximum value. 1 is a view showing a peak & hold (or level detection) partial discharge measurement method to which a conventional hardware filter is applied.

However, in the case of high-voltage power devices, since they are electrically connected to various devices and also have various noises that can be generated in the field, a noise removal technique is very important to measure and analyze the partial discharge signal.

In the case of the Peak & Hold(or Level Detecting) method, only a hardware filter can be used in front of the ADC to remove noise because it measures only the peak signal at the time in the phase of the applied voltage (60Hz) of the high voltage power device (frequency analysis and software filter cannot be applied) ).

In addition, the peak & hold method may have low diagnostic accuracy by using only information on the magnitude and number of occurrences corresponding to the phase among a lot of information included in the partial discharge signal. It is necessary to know the exact frequency information of the discharge and the frequency band of various noises generated in the field.

In addition, there is a disadvantage that several hardware filters must be used when signals with various frequency bands are measured simultaneously. There is this.

Meanwhile, a method of measuring a partial discharge signal based on high-speed sampling is also being attempted in order to use more information on the partial discharge signal, which is a high-frequency signal. 2 is a diagram illustrating a partial discharge measurement method based on high-speed sampling.

A two-dimensional map is generated by obtaining T (equivalent time) and F (equivalent frequency) values under the assumption that the partial discharge signals according to noise and defect types have different characteristics in the time and frequency domains. Through grouping, the corresponding group is visualized as a phase resolved partial discharge (PRPD) to classify the types of noise and defects.

3 is a PRPD (only max value according to phase) diagram of the measured raw data, FIG. 4 is a diagram showing the raw data in a T-F map, and FIGS. 5 to 8 are A~ shown in the T-F map of FIG. It is a PRPD diagram of group D.

However, the T-F Map is calculated by dividing the measured signal by a predetermined time window (length (or time) L). When two or more types of signals are measured within the corresponding time window, a problem arises as to which signal should be classified.

In addition, if the noise signal compared to the partial discharge signal is large within the corresponding time window, it may appear as a noise characteristic when calculating the T-F map. There is a problem that may appear as a third signal characteristic other than .

KR 10-0729856 B1

The present invention has been devised to solve the above-mentioned problems of the prior art, and provides a partial discharge diagnosis system and method that can make filters of various bands without additional cost through software filter application and can ensure high diagnostic accuracy. aim to do

In order to achieve the above object, a system for diagnosing partial discharge of electric power equipment according to the present invention includes a signal measuring unit, a frequency analyzing unit, a peak extracting unit, a filtering unit, and a diagnosis unit.

The signal measuring unit measures the partial discharge signal from the power device, the frequency analysis unit performs frequency analysis of the partial discharge signal, the peak extractor extracts a plurality of peaks in the order of magnitude in the frequency domain, and the filtering unit extracts a plurality of peaks The partial discharge signal is filtered for a frequency region corresponding to , and the diagnosis unit diagnoses the type of the partial discharge signal by comparing the signal characteristic of the filtered partial discharge signal with a preset reference signal characteristic.

According to such a configuration, it is possible to use information in more areas of the partial discharge signal, and to make filters of various bands without additional cost through software filter application.

In addition, by applying a software filter before determining the partial discharge, it is possible to secure high diagnostic accuracy even when different types of partial discharge signals overlap.

In this case, the diagnostic unit may further diagnose the noise by comparing the signal characteristics of the filtered partial discharge signal with the preset reference signal characteristics. According to this configuration, it is possible to further enhance the accuracy of diagnosis by excluding noise from the partial discharge signal.

The apparatus may further include a reference signal characteristic setting unit configured to set preset reference signal characteristics by machine learning from a plurality of partial discharge signals. According to such a configuration, it is possible to effectively set the characteristics of the reference signal used for diagnosing the partial discharge signal.

In this case, the reference signal characteristic setting unit may update the setting of the reference signal characteristics by using the filtered partial discharge signal and the diagnosis result. According to this configuration, the diagnosis result can be used again for a new diagnosis, so that a more effective partial discharge diagnosis can be performed.

In addition, the measurement of the partial discharge signal is performed for a plurality of periods of the power frequency, and the peak extractor may extract the largest peak for the same frequency among the peaks extracted for the plurality of periods as a representative peak. According to such a configuration, it is possible to perform a more accurate partial discharge diagnosis using partial discharge signals for a plurality of cycles.

In addition, the signal characteristic of the filtered partial discharge signal may include one or more of a time domain waveform characteristic, a frequency characteristic, and a PRPD pattern characteristic of the filtered partial discharge signal. According to such a configuration, it is possible to perform the partial discharge diagnosis of the power device more effectively and accurately by using various signal characteristics differentiated according to the partial discharge signal.

In addition, the invention implementing the system in the form of a method and a recording medium recording a computer readable program for executing the method are disclosed together.

According to the present invention, it is possible to use information in more areas of the partial discharge signal, and to make filters of various bands without additional cost through software filter application.

In addition, by applying a software filter before determining the partial discharge, it is possible to secure high diagnostic accuracy even when different types of partial discharge signals overlap.

In addition, it is possible to further enhance the accuracy of diagnosis by excluding noise from the partial discharge signal.

In addition, it is possible to effectively set the characteristics of the reference signal used for diagnosing the partial discharge signal.

In addition, since the diagnosis result can be used again for a new diagnosis, it is possible to perform a more effective partial discharge diagnosis.

In addition, it is possible to perform a more accurate partial discharge diagnosis by using the partial discharge signal for a plurality of cycles.

In addition, it is possible to perform the partial discharge diagnosis of the power device more effectively and accurately by using various signal characteristics differentiated according to the partial discharge signal.

1 is a view showing a peak & hold (or level detection) partial discharge measurement method to which a conventional hardware filter is applied.
2 is a diagram illustrating a partial discharge measurement method based on high-speed sampling;
3 is a PRPD diagram of measured raw data.
Fig. 4 is a diagram showing raw data on a TF map;
5 to 8 are PRPD diagrams of groups A to D shown in the TF map of FIG.
9 is a schematic block diagram of a system for diagnosing partial discharge of electric power devices according to an embodiment of the present invention;
10 is a schematic flowchart of a partial discharge diagnosis method performed in the power device partial discharge diagnosis system of FIG. 9;
Fig. 11 is a diagram of a measured original signal measured by a signal measuring unit;
12 is a diagram illustrating a representative peak extracted from the original measurement signal of FIG. 11 and an example of a frequency band corresponding to the peak;
13 and 14 are PRPD diagrams for a signal filtered by band 1 of FIG. 11 and the signal, respectively;
15 and 16 are PRPD diagrams for a signal filtered by band 4 of FIG. 11 and the signal, respectively;
17 to 19 are diagrams showing experimental data of PD size-based PRPD patterns, time domain waveforms, and frequency characteristics for each defect type simulated in GIS, respectively.

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings.

9 is a schematic block diagram of a partial discharge diagnosis system for a power device according to an embodiment of the present invention, and FIG. 10 is a schematic flowchart of a partial discharge diagnosis method performed in the partial discharge diagnosis system for a power device of FIG. 9 .

9 , the power device partial discharge diagnosis system 100 includes a signal measuring unit 110 , a frequency analyzing unit 120 , a peak extracting unit 130 , a filtering unit 140 , a diagnosis unit 150 , and a reference signal. and a characteristic setting unit 160 .

The signal measuring unit 110 measures a partial discharge signal in the power device, and the frequency analyzing unit 120 performs frequency analysis of the partial discharge signal. In this case, in order to use more information of the partial discharge signal, the signal may be measured by high-speed sampling, for example, the sampling rate may be 250 MS/s.

In this way, when high-speed sampling is used, more information (time, frequency domain) of the partial discharge signal can be used, and thus the accuracy of signal analysis can be increased.

In addition, filters of various bands can be made without additional cost through the application of software filters, so that noise and signals can be more effectively distinguished in terms of accuracy and economy.

The peak extractor 130 extracts a plurality of peaks in the order of magnitude in the frequency domain, and the filter 140 filters the partial discharge signal with respect to the frequency domain corresponding to the plurality of peaks. The frequency region corresponding to the peak is a frequency band of a preset range including the peak frequency, and setting of the frequency band range may be performed in advance by a user or the like.

In addition, the measurement of the partial discharge signal is performed for a plurality of cycles of the power frequency, and the peak extractor 130 may extract the largest peak for the same frequency among the peaks extracted for the plurality of cycles as a representative peak. According to such a configuration, it is possible to perform a more accurate partial discharge diagnosis using partial discharge signals for a plurality of cycles.

11 is a diagram of a measurement original signal measured by a signal measurement unit, and FIG. 12 is a diagram illustrating a representative peak extracted from the measurement original signal of FIG. 11 and an example of a frequency band corresponding to the peak. In FIG. 11 , a partial discharge signal due to a corona defect generated in the GIS can be confirmed, and in FIG. 12 , an example in which the peak extractor 130 extracts four frequency domains in descending order with respect to the peak size can be confirmed.

The diagnosis unit 150 compares the signal characteristics of the filtered partial discharge signal with a preset reference signal characteristic to diagnose the type of the partial discharge signal. In this case, the diagnosis unit 150 may further diagnose the noise by comparing the signal characteristics of the filtered partial discharge signal with the preset reference signal characteristics.

In addition, the signal characteristic of the filtered partial discharge signal may include one or more of a time domain waveform characteristic, a frequency characteristic, and a PRPD pattern characteristic of the filtered partial discharge signal. According to such a configuration, it is possible to perform the partial discharge diagnosis of the power device more effectively and accurately by using various signal characteristics differentiated according to the partial discharge signal.

13 and 14 are PRPD diagrams for a signal filtered by band 1 of FIG. 11 and a PRPD diagram for the signal, respectively, and FIGS. 15 and 16 are PRPD diagrams for a signal filtered by band 4 of FIG. 11 and the signal, respectively am.

13 and 14, it can be confirmed that the signal filtered by band 1 is noise, and it can be excluded during partial discharge diagnosis, and the signal filtered by band 4 according to FIGS. 15 and 16 is a partial discharge signal. can be confirmed, and by using this, it is possible to perform a diagnosis of partial discharge more effectively.

The reference signal characteristic setting unit 160 sets preset reference signal characteristics by machine learning from a plurality of partial discharge signals. According to such a configuration, it is possible to effectively set the characteristics of the reference signal used for diagnosing the partial discharge signal.

In this case, the reference signal characteristic setting unit 160 may update the setting of the reference signal characteristics by using the filtered partial discharge signal and the diagnosis result. According to this configuration, the diagnosis result can be used again for a new diagnosis, so that a more effective partial discharge diagnosis can be performed.

17 to 19 are diagrams showing experimental data of PD size-based PRPD patterns, time domain waveforms, and frequency characteristics for each defect type simulated in GIS, respectively. All defect types show corona defects, particle defects, and floating cases.

As can be seen in FIGS. 17 to 19 , since the characteristics of the partial discharge signals for each noise and defect are different in time and frequency domains, they can be set and used for partial discharge diagnosis.

In summary, an object of the present invention is to remove noise and classify types of defects for precise analysis of partial discharge signals that occur when insulation deteriorates in high voltage power equipment. In order to use more information on the partial discharge signal, the signal is measured by high-speed sampling, and the main frequency is automatically calculated through frequency analysis and a filter of the corresponding frequency band is applied.

In addition, useful partial discharge factors are calculated using the filtered data, and noise, partial discharge, and defect types are classified through comparison (learning) with the existing noise and partial discharge DB.

Compared with the conventional Peak & Hold method, more information (time, frequency domain) of the partial discharge signal can be used, and there is no additional cost through software filter application (requires software-->multiplication, addition calculation only) Filters of various bands can be made, which is effective in distinguishing noise and signals.

In addition, when compared with the conventional T-F map method, higher diagnostic accuracy can be secured by applying a software filter before calculating the effective partial discharge factor (equivalent time, equivalent frequency in the prior art).

Although the present invention has been described with reference to some preferred embodiments, the scope of the present invention should not be limited thereto, but should also extend to modifications or improvements of the above embodiments supported by the claims.

100: power equipment partial discharge diagnostic system
110: signal measuring unit
120: frequency analysis unit
130: peak extraction unit
140: filtering unit
150: diagnostic unit
160: reference signal characteristic setting unit

Claims (13)

a signal measuring unit for measuring a partial discharge signal from a power device;
a frequency analyzer for performing frequency analysis of the partial discharge signal;
a peak extractor for extracting a plurality of peaks in the order of magnitude in the frequency domain;
a filtering unit filtering the partial discharge signal in a frequency region corresponding to the plurality of peaks; and
A power device partial discharge diagnosis system including a diagnosis unit for diagnosing a type of the partial discharge signal by comparing the signal characteristics of the filtered partial discharge signal with a preset reference signal characteristic,
and the diagnosis unit compares the signal characteristics of the filtered partial discharge signal with a preset reference signal characteristic to further diagnose the noise.
delete The method according to claim 1,
The power device partial discharge diagnosis system according to claim 1, further comprising a reference signal characteristic setting unit configured to set the preset reference signal characteristics by machine learning from a plurality of partial discharge signals.
4. The method according to claim 3,
and the reference signal characteristic setting unit updates the setting of the preset reference signal characteristics by using the filtered partial discharge signal and the diagnosis result.
5. The method according to claim 4,
The measurement of the partial discharge signal is performed for a plurality of periods of the power frequency,
The peak extractor extracts the largest peak for the same frequency among the peaks extracted for the plurality of periods as a representative peak.
6. The method of claim 5,
The signal characteristic of the filtered partial discharge signal comprises at least one of a time domain waveform characteristic, a frequency characteristic, and a PRPD pattern characteristic of the filtered partial discharge signal.
A signal measuring step of measuring a partial discharge signal in the power device;
a frequency analysis step of performing frequency analysis of the partial discharge signal;
A peak extraction step of extracting a plurality of peaks in the order of magnitude in the frequency domain;
a filtering step of filtering the partial discharge signal with respect to a frequency region corresponding to the plurality of peaks; and
A method for diagnosing partial discharge of electric power equipment including a diagnosis step of diagnosing a type of the partial discharge signal by comparing the signal characteristics of the filtered partial discharge signal with a preset reference signal characteristic,
The diagnosing step further diagnoses noise by comparing the signal characteristics of the filtered partial discharge signal with a preset reference signal characteristic.
delete 8. The method of claim 7,
The method for diagnosing partial discharge of electric power equipment according to claim 1, further comprising: a reference signal characteristic setting step of setting the preset reference signal characteristics by machine learning from a plurality of partial discharge signals.
10. The method of claim 9,
The method for diagnosing the partial discharge of a power device, wherein the setting of the reference signal characteristic includes updating the setting of the preset reference signal characteristic by using the filtered partial discharge signal and the diagnosis result.
11. The method of claim 10,
The measurement of the partial discharge signal is performed for a plurality of periods of the power frequency,
In the peak extraction step, the largest peak for the same frequency among the peaks extracted for the plurality of periods is extracted as a representative peak.
12. The method of claim 11,
The signal characteristic of the filtered partial discharge signal includes at least one of a time domain waveform characteristic, a frequency characteristic, and a PRPD pattern characteristic of the filtered partial discharge signal.
A recording medium in which a computer readable program for executing the method of any one of claims 7 and 9 to 12 is recorded.

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