KR102377939B1 - Partial discharge monitoring and diagnosis system for distribution board using ultra frequency and high frequency current transformer signal - Google Patents

Abstract

The present invention relates to a system for monitoring and diagnosing partial discharge of a distribution panel using UHF and HFCT electrical signals, capable of diagnosing a type of partial discharge. The system of the present invention comprises: a detection device for detecting partial discharge; a signal processing device for converting a signal transmitted from the detection device into a digital signal to transmit the same; and a monitoring device for analyzing and diagnosing the digital signal transmitted from the signal processing device.

Description

DISCHARGE MONITORING AND DIAGNOSIS SYSTEM FOR DISTRIBUTION BOARD USING ULTRA FREQUENCY AND HIGH FREQUENCY CURRENT TRANSFORMER SIGNAL

The present invention relates to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, and more particularly, high-voltage cables, transformers, GIS (Gas Insulated Switchgear), switchgear, switchgear, electric motor, generator, etc. HFCT (High Frequency Current Transformer) that detects electric signals of high-frequency current generated by UHF (Ultra High Frequency) sensor and partial discharge for partial discharge due to poor insulation, poor connection, or disconnection in power facilities in the housed switchboard It relates to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals that can monitor and diagnose the occurrence of partial discharge while quickly detecting it using a sensor.

In general, partial discharge refers to any one of power equipment systems, such as power reception facilities and high-voltage switchgear, high-voltage cables, transformers, GIS (Gas Insulated Switchgear), switchgear, and power reception facilities installed in various industries and power system substations. As a generic term for the discharges generated in parts, corona discharges occurring near the tip of an electrode, creepage discharges occurring along the surface of an insulator, void discharges occurring in voids in the insulator, and the like are mentioned.

It is very important to detect abnormalities in power facilities, monitor the degree of deterioration of insulators, and predict the repair time, and such prediction and management are possible with the measurement and monitoring of partial discharge. For this purpose, to measure partial discharge in various power equipment such as high voltage cables, transformers, GIS (Gas Insulated Switchgear), switchgear, power receiving equipment, high voltage panel, low voltage panel, motor control panel, switchboard, etc. Various devices are being used for this.

As a technology for diagnosing or monitoring partial discharge in power facilities, Korean Patent Publication No. 10-1908706 discloses a partial discharge diagnosis and comprehensive management system for power infrastructure facilities.

The technology is mounted on the power infrastructure, the control unit capable of data communication; a partial discharge sensing unit which detects the partial discharge signal generated in the power infrastructure and transmits the detection signal to the control unit; a display unit mounted on the power infrastructure and configured to receive an abnormal signal from the control unit and issue an alarm when the detected signal of the partial discharge sensing unit is an abnormal signal; and a smart actuator for reducing the load of the power infrastructure when the abnormal signal is detected.

In addition, a partial discharge monitoring system of a power device is disclosed in Korean Patent Registration No. 10-2117938.

The technology includes a sensor unit installed inside the switchgear to emit overpressure and at the same time detect an electrical signal of a high-frequency current generated by partial discharge, wherein the sensor unit includes a non-conductive material unit; a first pattern part disposed on one surface of the non-conductive material part, having a first shape, and detecting an electrical signal; and a second pattern part disposed on the one surface, having a second shape and detecting an electrical signal, wherein when the second shape is rotated 180 degrees with respect to the first axis, the rotated second shape is the second shape It is configured to be symmetrical with the first shape with respect to a second axis perpendicular to the first axis.

However, in the above technologies, a sensor for detecting a partial discharge has a general configuration or a pattern is analyzed to sense, and it is difficult to identify a type of a partial discharge based on a sensing signal.

Registered Patent Publication No. 10-1908706 (2018. 10. 10.) Registered Patent Publication No. 10-2117938 (May 27, 2020)

The present invention has been devised to solve the above problems, and the problem to be solved in the present invention is to use UHF and HFCT electrical signals capable of monitoring and diagnosing the occurrence of a partial discharge by rapidly detecting the partial discharge generated in power facilities. It is to provide a switchgear partial discharge monitoring and diagnosis system.

In addition, another object to be solved by the present invention is to provide a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals that can detect the type of partial discharge and appropriately respond according to the detected type of partial discharge. .

The switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention for solving the above problems is any one of a high-voltage cable, a transformer, a GIS (Gas Insulated Switchgear), a switchgear, a power receiving facility, and a switchboard a detection device for detecting partial discharge occurring in one power facility; a signal processing device for converting the signal transmitted from the detection device into a digital signal and transmitting the converted signal; and a monitoring device for diagnosing by analyzing the digital signal transmitted from the signal processing device, wherein the detection device is installed in the switchboard and generated by partial discharge in the switchboard using microwaves of 300 MHz to 3 GHz A UHF (Ultra High Frequency) module for detecting an electric signal of very high frequency and an HFCT (High Frequency Current Transformer) module installed on the ground wire of the switchboard to detect an electric signal of a high frequency current generated by partial discharge do it with

At this time, the UHF module is configured to include a UHF sensor and a UHF converter for sampling by detecting a peak signal of the signal detected by the UHF sensor.

In addition, the UHF converter includes an amplifier for amplifying the signal output from the UHF sensor; a first diode for blocking a low frequency signal with respect to a signal output from the amplifier; a first capacitor charged by a peak voltage when the signal output from the first diode is a positive pulse signal; A second capacitor for charging a voltage with respect to a pulse signal between the first diode and the first capacitor, and a shunt resistor connected in parallel with the second capacitor to limit the charging current to the second capacitor and discharge the charging voltage characterized in that

Here, the UHF sensor may be configured as an antenna of an M-shaped microstrip structure.

In addition, the HFCT module is configured to include an HFCT sensor and an HFCT converter for adjusting the signal detected by the HFCT sensor.

At this time, the HFCT converter is a first amplifier for amplifying the signal output from the HFCT sensor; and a band-pass filter for filtering the signal amplified by the first amplifier and a second amplifier for amplifying the signal output from the band-pass filter.

In addition, the band pass filter is characterized in that the frequency of the 2 ~ 10 MHz band pass.

In addition, the signal processing apparatus includes an AD converter for converting each analog signal output from the detection apparatus into a digital signal; a complex signal processor for sequentially outputting a plurality of digital signals output from the AD converter according to set logic; a temporary memory for temporarily storing the digital signal output from the composite signal processor; a trigger input device providing a trigger signal; RTC providing real-time time; and a digital signal processor for transmitting the digital signal of the temporary memory to the monitoring device based on the trigger signal provided from the trigger input device based on the real time time provided from the RTC.

In addition, the monitoring device includes: a diagnostic unit for diagnosing partial discharge with respect to the digital signal transmitted from the signal processing device; and an output unit for transmitting the diagnosis result by the diagnosis unit to an external device or an external terminal (HMI).

In this case, the diagnosis unit comprises: an identification element extraction module for extracting an identification element from the detected data that is the received digital signal; a pattern classification module for classifying patterns of identification elements into clusters by using identification element vectors for the identification elements extracted by the identification element extraction module; a comparison module for comparing which cluster of comparison data stored in advance with the detection data through the pattern classification module, and a diagnostic module for diagnosing the type of partial discharge with respect to the cluster compared in the comparison module; is comprised of

In addition, the comparison data is learned by mixing real data detected by simulation and fake data randomly generated, and the discriminator of the learning uses CNN (Convolutional Neural Networks) It is characterized in that it is discriminated.

According to the present invention, the partial discharge generated in the switchboard is detected by a single or a plurality of UHF sensors to diagnose the type of the partial discharge in a remote monitoring device, and the location of the partial discharge can be determined according to the diagnosis result.

In addition, there is an advantage in that it is possible to diagnose a partial discharge suitable for the recognized module by automatically recognizing a module for each frequency for performing various types of diagnostic methods as a partial discharge diagnosis for each characteristic.

1 is a schematic configuration diagram of a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
2 is a block diagram of a UHF module applied to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
3 is a block diagram of a UHF sensor applied to a switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
4 is a graph showing the impedance characteristics, radiation pattern, reflection coefficient and voltage standing wave ratio (VSWR) of the UHF sensor applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
5 is a circuit diagram of a UHF converter applied to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
6 is a block diagram of an HFCT module applied to a switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
7 is a circuit diagram of an HFCT converter applied to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
8 is a block diagram of a signal processing device applied to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
9 is a block diagram of a monitoring device applied to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
10 is a two-dimensional image of a partial discharge type derived by the PRPD (Phase Resolved Partial Discharge) method of the analysis unit applied to the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
11 is a two-dimensional image of a partial discharge type derived by an amplification method in the Average Projection Phase Resolved Partial Discharge (APPRPD) method of the analysis unit applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
12 is a view showing a process of generating simulated data through DCGAN applied to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
13 is a configuration table of experimental data including simulated data applied to the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention;
14 is a graph of a PRPD three-dimensional waveform using actual data of noise (a), void discharge (b), corona discharge (c) and floating electrode discharge (d);
15 is a graph of a phase resolved pulse sequence (PRPS) three-dimensional waveform of data obtained by mixing real data and simulated data of noise (a), void discharge (b), corona discharge (c) and floating discharge (d);
16 is a structure of a CNN (Convolutional Neural Networks) used as an algorithm for determining a discharge type in the present invention;
17 is a table showing the learning accuracy of PRPS-based comparison data applied to the partial discharge monitoring and diagnosis system of power facilities using ultra-high frequency and high-frequency electrical signals according to the present invention;
18 is a table showing the learning accuracy of PRPD-based comparison data applied to the intelligent switchboard partial discharge monitoring and diagnosis system using ultra-high frequency electrical signals according to the present invention;
19 is a table showing the learning accuracy of APRPPD-based comparison data applied to the intelligent switchboard partial discharge monitoring and diagnosis system using ultra-high frequency electrical signals according to the present invention.

Next, a preferred embodiment of the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention will be described in detail with reference to the drawings.

Hereinafter, the same reference numerals are used for technical elements having the same function, and repeated detailed descriptions are omitted to avoid overlapping descriptions.

In addition, the examples described below are illustratively shown in order to effectively show the preferred embodiments of the present invention, and should not be construed to limit the scope of the present invention.

The present invention is a high-voltage cable, transformer, GIS (Gas Insulated Switchgear), switchgear, switchgear, electric motor, generator, etc. in power facilities such as partial discharge due to poor insulation, poor connection or disconnection, etc. Ultra High Frequency (UHF, Ultra High Frequency) ) It relates to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals that can diagnose the type of partial discharge while quickly detecting it using a sensor and a High Frequency Current Transformer (HFCT).

1 is a schematic configuration diagram of a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

1, the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention includes a detection device 100 including a UHF module 101 and an HFCT module 103, a signal processing device ( 200) and a monitoring device 300 .

In this case, the detection device 100 and the signal processing device 200 may be installed in plurality corresponding to the number of switchboards, and are configured to be expandable.

The detection device 100 includes a UHF (Ultra High Frequency) module 101 and an HFCT (High Frequency Current Transformer, high frequency current transformer) module 103, and the UHF module 101 receives a microwave of 300 MHz to 3 GHz. Through the detecting UHF sensor, the electric signal of the very high frequency generated by the partial discharge in the switchboard is detected, and the HFCT module 103 is attached to the ground wire of the switchboard and the electric signal of the high frequency current generated by the partial discharge through the HFCT sensor to detect

The reason for applying the UHF module 101 is that it has a high signal-to-noise ratio with high detection sensitivity and resistance to external electromagnetic interference. That is, around the switchboard (power facility) including the HV facility is exposed to noise in a low frequency range (less than 200 MHz) such as continuous white noise, and errors due to such noise can be minimized. In addition, in the case of power equipment finished with a steel tank or shell, etc., due to the shielding effect of the metal structure, external electromagnetic interference from the surrounding corona emission can be effectively mitigated. Of course, when the UHF sensor is externally mounted, electromagnetic waves of telecommunication systems such as TV and radio broadcasting may affect the accuracy at the location where partial discharge is monitored, but compared to other sensors that detect partial discharge, it is less sensitive to noise. There is an advantage in that errors can be minimized.

Partial discharge is an impulse signal that can distinguish and extract the contaminated signal. In addition, the UHF module 101 including the UHF sensor is applied in the present invention because the impulse signal due to the partial discharge is not only related to the amplitude of the emission but also varies depending on the location.

A plurality of the UHF modules 101 are installed in one selected power facility, and are provided as information for estimating the location according to the partial discharge time difference detected at each installed location.

The HFCT module 103 is also a sensor widely used for partial discharge detection, and is very useful for location and identification of a partial discharge source. The HFCT module 103 is composed of an induction coil having a ferromagnetic core suitable for measurement of transient signals as partial discharge or pulse noise interference, and is installed on a grounding conductor.

2 is a block diagram of a UHF module applied to a switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

Referring to FIG. 2 attached, the UHF module 101 is configured to include a UHF sensor 110 and a UHF converter 120 .

The UHF sensor 110 can be viewed as an antenna because it is a sensor necessary for receiving electromagnetic waves induced from the partial discharge source. That is, the UHF sensor 110 is used as an energy conversion device for converting the coupled electromagnetic wave signal generated by the source generated by the partial discharge into a current or voltage signal.

The UHF sensor 110 is widely applied to power equipment such as power transformers, cables, rotating machines, switch gears, and gas insulated substations to detect partial discharge, and the sensor arrangement is different for each application field.

A general antenna is divided into a monopole antenna that is vertically erected on a base plate and a microstrip antenna that is configured in the form of a base plate and a horizontal plate.

The monopole antenna is widely used because of its good radiation pattern and appropriate size, but has a disadvantage in that the operating bandwidth is narrow and information loss is large.

The microstrip antenna has the advantages of small thickness, low weight, and low manufacturing cost, but it is difficult to analyze and has a narrow bandwidth along with dielectric loss.

The UHF sensor 110 should be designed in consideration of parameters such as S-parameter, voltage standing waveform ratio, input impedance, and surface current distribution and directionality (gain of E-plane and H-plane).

3 is a view showing a UHF sensor applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

3, the UHF sensor 110 applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention is composed of an M-shaped microstrip structure antenna, and has a predetermined thickness. a dielectric substrate 111 having a branch, an M-shaped radiator 112 formed on the upper surface of the dielectric substrate 111, disposed on the upper surface of the dielectric substrate 111, and connected to the lower end of the center of the radiator 112 toward the lower side It is configured to include a long feed line 113 and a ground plate 114 formed on the lower surface of the dielectric substrate 111 .

The dielectric substrate 111 is made of a non-conductive material, and the radiator 112 is symmetrically formed, and notches 112a and 112b inclined to the side are formed.

4 shows the impedance characteristics, radiation pattern, reflection coefficient, and voltage standing wave ratio (VSWR) of the UHF sensor applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

The impedance characteristic (FIG. 4(a)) of the UHF sensor 110 applied to the present invention was measured by estimating the input frequency.

Looking at the radiation pattern (FIG. 4(b)) of the UHF sensor 110, it was measured that the partial discharge signal (electromagnetic wave signal) can be received in almost all directions. At this time, it was measured that a signal orthogonal to the UHF sensor 110 can be detected with high sensitivity, and it was measured that the directionality can be further improved by appropriately adjusting the location installed in the power facility.

It shows the allowable frequency bandwidth from 680 MHz to 1.53 GHz through the reflection coefficient ((c) of FIG. 4) and the voltage standing wave ratio ((d) of FIG. 4) of the UHF sensor 110, and three resonant frequencies (690 MHz, 1.09 GHz, 1.51 GHz).

5 is a circuit diagram of a UHF converter applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

The UHF converter 120 performs a function of detecting and sampling the peak signal of the signal detected by the UHF sensor 110. Referring to FIG. 5, the UHF converter 120 is the UHF sensor ( an amplifier 121 amplifying the signal output from 110); a first diode (122) for blocking a low-frequency signal with respect to the signal output from the amplifier (121); a first capacitor 123 charged with a peak voltage when the signal output from the first diode 122 is a positive pulse signal; a second capacitor 124 for charging a voltage with respect to a pulse signal between the first diode 122 and the first capacitor 123; and a shunt resistor 125 connected in parallel with the second capacitor 124 to limit a charging current to the second capacitor 124 and discharge a charging voltage, and a reverse voltage applied to the first capacitor 124 . A reversal prevention diode 126 is configured to prevent .

In more detail, the signal detected by the UHF sensor 110 is amplified by 40 dB by the amplifier 121 . The amplified signal passes through the first diode 122 and is integrated by the first capacitor 123 and the second capacitor 124 .

At this time, when the polarity of the signal output from the UHF sensor 110 is positive, the voltages of the first capacitor 123 and the second capacitor 124 rise, and the raised voltage is discharged by the shunt resistor 125 . .

In the above process, the peak hold value (envelope) of the positive signal (pulse) output from the UHF sensor 110 is recorded in the first capacitor 123 , and the peak hold value can be sampled.

This peak-hold detection has the advantage of significantly lowering the sampling rate requirement.

6 is a view showing the configuration of the HFCT module applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

Referring to the accompanying FIG. 6 , the HFCT module 103 is configured to include an HFCT sensor 130 and an HFCT converter 140 .

The HFCT sensor 130 is an inductive sensor also called a High Frequency Current Transformer, and is composed of a donut-shaped ferromagnetic iron core and an induction coil wound around the iron core, and includes a sheath and grounding of a power cable. It is installed on various grounding wires such as between terminals or between the enclosure of high voltage equipment and the grounding terminal.

That is, the HFCT sensor 130 is disposed through the ground line, and when a current flows through the ground line, the current of the ground line is induced in the induction coil, and a voltage due to the current induced in the induction coil is output. At this time, the voltage induced in the induction coil, that is, the secondary side is output in proportion to the change in the primary side current and the mutual inductance, which is a proportional constant.

However, while the donut-shaped coil sensor has a wide frequency bandwidth, it has a disadvantage in that it includes a non-linear element that depends on temperature and magnetic flux density. Therefore, the HFCT, which is a high-frequency current sensor applied to the present invention, should be designed to detect a specific frequency.

In other words, at the initial stage of discharge of the current pulse, a rising time of several nanoseconds is shown, which outputs an important spectral component of several hundred MHz or more.

The high frequency range frequency component of the discharge pulse generated according to the partial discharge is filtered when it propagates to the ground conductor to which the HFCT sensor is connected. Accordingly, when partial discharge occurs, it must be designed to have a response characteristic of 40 MHz or less to detect the partial discharge.

For example, the iron core of the HFCT sensor 130 is made of an alloy of nickel and zinc having high magnetic permeability, and the induction coil is configured in the form of a toroid made of brass material so as to be wound dozens of times on the iron core. . Preferably, the induction coil may be configured to be wound within the range of 20 to 50 times.

According to this configuration, the response characteristic of the HFCT sensor 130 has a range of 100 kHz to 30 MHz.

The HFCT converter 140 controls the signal detected by the HFCT sensor 130, and FIG. 7 shows a circuit diagram of the HFCT converter applied to the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention. .

7, the HFCT converter 140 includes a first amplifier 141 for amplifying the signal output from the HFCT sensor 130, and a bandpass for filtering the signal amplified by the first amplifier 141. It is configured to include a filter 142 and a second amplifier 143 amplifying the signal output from the band pass filter 142 .

Here, the HFCT converter 140 is configured to be amplified again after amplification and filtering because the signal output from the HFCT sensor 130 is very weak within the range of millivolts. In this case, the band-pass filter 142 may be a second-order band-pass filter having a frequency range of 2 MHz to 10 MHz in order to avoid narrow band interference.

According to the above configuration, the signal output from the HFCT converter 140 can achieve a relatively improved signal-to-noise (SNR) effect by amplifying and filtering and then amplifying again.

8 shows the configuration of a signal processing device applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

8, the signal processing device 200 applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention is an AD converter 210, a composite signal processor 220, a temporary memory ( 230 ), a trigger input unit 240 , an RTC ( 250 , Real-Time Clock) and a digital signal processor 260 .

The AD converter 210 converts each analog signal output from the detection device 100 into a digital signal.

The AD converter 210 is configured in plurality to correspond to the number of installed sensors.

That is, corresponding to the number of UHF sensors 110 of the UHF module 101 constituting the detection device 100 and the number of HFCT sensors 130 of the HFCT module 103, the AD converter 210 is configured in plurality , each converts the analog signal transmitted from the sensor into a digital signal and outputs it.

Such, the AD converter 210 corresponds to the bit of the digital signal converted according to the amplitude of the peak hold signal output from the UHF converter 120 and the amplitude of the amplified signal output from the HFCT converter 140 to the upper bit and the lower bit. It may be configured to be converted into a digital signal by dividing and converted into a digital signal by combining each of the converted high-order bits and low-order bits.

More specifically, a pre-processing unit for classifying a high-order bit and a low-order bit corresponding to the number of bits of the converted digital signal, and comparing the size of the input analog signal to which range from the high-order bit or the low-order bit, and the pre-processing unit Based on the comparison result in , a bit arithmetic unit composed of an upper bit arithmetic module and a lower bit arithmetic module, and a bit arithmetic unit composed of a high bit operation module It is configured to include a digital signal generator for generating a signal.

The composite signal processor 220 performs a function of sequentially outputting a plurality of digital signals output from the AD converter 210 according to a set logic.

That is, the digital signal output from the AD converter 210 is a plurality of parallel signals corresponding to the number of sensors. Accordingly, when each digital signal is sequentially processed, a time difference is generated according to the precedence of the signal, and an error is generated in subsequent processing (eg, partial discharge detection position estimation by time difference, etc.) due to the generated time difference. .

Also, the composite signal processor 220 is configured to provide a sampling clock operated by the AD converter 210 .

The temporary memory 230 temporarily stores the digital signal output from the composite signal processor 220 .

That is, the temporary memory 230 performs a function of a data buffer for temporarily storing signals, and may be formed of SRAM.

The trigger input unit 240 provides a trigger signal, and in the process of analyzing and diagnosing the signal of the partial discharge, the phase of the signal of the partial discharge and the power provided by the corresponding power facility has a close relationship. That is, since the occurrence of the partial discharge affects the phase of the power provided from the power facility, the signal of the partial discharge should be triggered based on the phase angle of the fixed power source.

At this time, the trigger signal output from the trigger input unit 240 may be configured to be input through a comparator 245 that compares the phase angle of the power.

The RTC (Real-Time Clock, 250) provides real-time time, and is provided as information for detecting the location of the partial discharge through a time deviation between the detected times while providing the time at which the partial discharge occurred.

The digital signal processor 260 transmits the digital signal of the temporary memory 230 to the monitoring device 300 based on the trigger signal provided from the trigger input unit 240 based on the real time time provided from the RTC 250 . While performing the function, the digital signal is stored in the memory 265 .

The monitoring device 300 performs a function of diagnosing by analyzing the digital signal transmitted from the signal processing device 200 .

9 shows the configuration of a monitoring device applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

9, the monitoring device 300 applied to the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention includes a signal receiving unit 310, a diagnosis unit 320 and an output unit 330. is comprised of

The signal receiving unit 310 performs a function of receiving the digital signal transmitted from the signal processing apparatus 200 .

The digital signal received by the signal receiver 310 may contain noise and distortion in the process of being transmitted, and the received digital signal recovers a signal waveform through a demodulator, and determines the signal transmitted through the digital detector will do

The diagnosis unit 320 performs a function of diagnosing partial discharge with respect to the digital signal transmitted from the signal processing apparatus 200 .

Accordingly, the diagnosis unit 320 selects an identification element vector for the identification element extracted from the identification element extraction module 321 and the identification element extraction module 321 for extracting identification elements from the detected data that is the received digital signal. A pattern classification module 322 for classifying the patterns of identification elements by cluster using the pattern classification module 323, a comparison module 323 for comparing which cluster of the comparison data stored in advance with the detection data through the pattern classification module 322, and and a diagnosis module 324 for diagnosing a type of partial discharge with respect to the detected data for the cluster derived from the comparison module 323 .

Here, the comparison data is pre-stored partial discharge data for comparing the detected data, which is a transmitted digital signal, and the comparison data is generated through the extraction module 321 and the pattern classification module 322 .

In addition, noise, void discharge, corona discharge, floating discharge, etc. are diagnosed as the type of partial discharge diagnosed by the diagnostic unit 320 .

Noise is a steady state in which discharge does not occur. If a noise removal technique such as shielding is not used, the UHF antenna captures frequency signals that are constantly generated by military units, mobile phones, and broadcasting. Minimizing noise is most important in partial discharge diagnosis.

Void discharge is caused by impurities with low dielectric strength inside the insulator. In general, these impurities are often gaseous, and are called voids (voids) and occur in the insulator. There are those in which voids are formed inside the insulator and those that occur at the interface between the electrode and the insulator. When voids exist in solid or liquid insulators, if a high voltage such as alternating current or shock wave is applied, the dielectric constant in the voids is smaller than that of the insulator, so discharge occurs in this part. In addition, when a gap is formed at the boundary between the spacer insulator and the semiconducting layer, a kind of void is formed and a large discharge occurs. This discharge is also called internal discharge.

Corona discharge is a discharge in which an electric field is severely concentrated at the front end of the electrode when the voltage is raised when a sharp-edged electrode is present in an insulating material of a gas or liquid. Needle corona discharge occurs faster at negative (-) voltage than positive (+) voltage. In AC voltage, corona discharge often occurs only in half cycle of negative (-) pulse.

Floating electrode discharge is a typical partial discharge that can occur in an insulator. The part where the electrical connection to the busbar is loose or not electrically connected is called the floating part or floating electrode, and the part inside the connection panel is called a floating electrode discharge. It is a discharge that is generated because the bus bar is not electrically connected and is not sturdy.

In order to analyze the signals for noise, void discharge, corona discharge, and floating discharge according to phases, a phase resolved partial discharge (PRPD) method is applied.

The PRPD method is an analysis method using the phase of the pulse, the size of the pulse, and the pulse frequency. The PRPD method is an effective method for analyzing the cause of the partial discharge by using that the partial discharge has a constant pattern unlike noise.

When the detected signal is expressed as a TF map of frequency and time, signals of the same type form a cluster on the graph.

Points expressed on the TF map indicate peak values and phase angles of similar signals, and the types of partial discharges can be distinguished by calculating a PRPD pattern composed of clustered signals.

The PRPD method may be displayed as a graph such as 2D or 3D, and pixels of the PRPD pattern may be used as input by calculating statistics.

In this case, the identifiers mainly used in the pixels of the PRPD pattern are skewness, kurtosis, and cross-correlation. In the above, the cross-correlation coefficient means the cross-correlation between the positive polarity and the negative polarity pattern.

Accordingly, the skewness is expressed by the following Equation 1 using the distribution function and the phase value at which the partial discharge occurs first.

Equation 1)

Here, S _k is the skewness, N _T is the total number of discharges, x _i is the size of the partial discharges, x _m is the average value of the partial discharges, and σ is the variance.

In addition, the kurtosis is expressed by the following Equation (2).

Equation 2)

Here, K _u is the kurtosis, N _T is the total number of discharges, x _i is the size of the i-th partial discharge, x _m is the average value of the partial discharges, and σ is the variance.

In addition, the cross-correlation coefficient is expressed by the following Equation (3).

Equation 3)

Here, CC is the cross-correlation coefficient, n is the total number of discharges, x _i is the size of the i-th partial discharge, and y _i is the phase of the i-th partial discharge.

In the PRPD analysis process, partial discharge signals generated during one cycle for a phase of 0 ° to 360 ° are collected at a sample period of 30 MS/s, and the collected signals are calculated using a peak hold method.

The calculated signals are accumulated and displayed as a graph after going through an arrangement process, and each graph and data are saved as a graphic file for analysis and confirmation of the discharge waveform.

In the present invention, a two-dimensional image of each discharge was analyzed in order to distinguish noise, void discharge, corona discharge, and floating discharge.

In the corona discharge, when the potential difference between the electrodes reaches the spark voltage, both electrodes are short-circuited by the spark discharge. It means a discharge in a state in which magnetic flux discharge is first generated only in the current part and insulation is not destroyed in other parts, and is called corona, partial breakdown, or incomplete breakdown.

Void discharge occurs when high voltage is applied when air bubbles or voids are included in solid and liquid insulating materials. Since the dielectric strength of is lower than that of a solid or liquid, it is a locally generated discharge inside the void. Such void discharge is generated in the bubbles or voids inside the insulator, and even when impurities having different dielectric constants are included in the dielectric constant, a local strong electric field causes dielectric breakdown.

Floating electrode discharge is a typical partial discharge that can occur in an insulator. The part where the electrical connection to the busbar is loose or not electrically connected is called the floating part or floating electrode, and the part inside the connection panel is called a floating electrode discharge. It is a discharge that is generated because the bus bar is not electrically connected and is not sturdy.

For each discharge, a two-dimensional image is constructed with the period on the vertical axis and the phase on the horizontal axis, and the pixel value in the constructed two-dimensional image is expressed to have a discharge size. Shows an image in which two lines are parallel. This means that the floating electrode discharge and the void discharge discharge in the same phase in any period, and in the case of the floating electrode discharge, the vertical axis line is relatively narrow and clear compared to the void discharge, so floating electrode discharge and void discharge can be distinguished. .

Noise appears over the entire range, and it can be confirmed that corona discharge occurs when increasing to a positive voltage or decreasing to a negative voltage.

For each discharge, a two-dimensional image is constructed with the period on the vertical axis and the phase on the horizontal axis, and the pixel value in the constructed two-dimensional image is expressed to have a discharge size, creepage discharge and void discharge are Shows an image in which the two are parallel. This means that creepage discharge and void discharge discharge in the same phase in any period, and in the case of creepage discharge, the vertical axis line is relatively narrow and clear compared to the void discharge, so creepage discharge and void discharge can be distinguished.

Noise appears over the entire range, and it can be confirmed that corona discharge occurs when increasing to a positive voltage or decreasing to a negative voltage.

10 shows a two-dimensional image of the partial discharge type derived by the PRPD method of the analysis unit applied to the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

The PRPS method analyzes a signal based on time information, whereas the PRPD analyzes a signal based on the number of times a discharge has occurred. show

11 shows a two-dimensional image of a partial discharge type derived by the Average Projection Phase Resolved Partial Discharge (APPRPD) technique of the analysis unit applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

The APPRPD technique was devised to compensate for the shortcomings of the existing PRPD technique. It reduces the noise value with a high frequency of occurrence and expands and analyzes the discharge signal with a low frequency of occurrence.

11 (a) is a two-dimensional image of noise, (b) void discharge, (c) corona discharge, and (d) floating discharge. The horizontal axis is the phase and the vertical axis is the magnitude of the partial discharge signal.

Compared with the two-dimensional image of the partial discharge detected by the PRPD method of FIG. 10, referring to the attached FIG. 11, it can be seen that the signal of the partial discharge is displayed more clearly, and accordingly, there is considerable discrimination power in distinguishing the types of discharge. means that it can be given.

The identification factors set in the identification factor extraction module 321 include skewness (Sk), kurtosis (Ku), correlation coefficient (CCk), the ratio between the discharge amounts (Q), the number of discharges (N) and the maximum discharge size. It is configured including the ratio (Qmax).

The skewness is a measure of the degree of deviation from the central axis compared to the normal distribution, and is calculated as a positive value (+) when the distribution is biased toward the right side from the center of the whole.

The pattern classification module 322 regards each of the identification elements extracted by the identification element extraction module 321 as one selected vector, and performs a function of grouping vectors having a similar pattern.

For example, an identification element group consisting of n identification elements can be viewed as one vector, and since the dimension of the vector is n elements, it becomes an n-dimensional identification element vector. That is, when 6 identification elements are calculated from the discharge pulse signal detected 30 times from the same voltage and the same electrode system, 30 6-dimensional identification element vectors are generated. The generated 30 vectors each have slightly different values, but generally form a constant group, and this group is defined as a cluster in the present invention.

The comparison module 323 performs a function of comparing in which cluster of the comparison data stored in advance the detection data is included through the pattern classification module 322 .

That is, the comparison module 323 stores in advance a cluster of identification element vectors for each of noise, void discharge, corona discharge, and floating discharge, and performs an operation on the detection data detected by the detection device 100 to determine the identification element After deriving the vector, the derived identification element vector is compared with the identification element vector in the pre-stored cluster, and a cluster in which the identification element vector having the smallest angle between the two vectors is derived.

That is, the comparison module 323 stores in advance a cluster of identification element vectors for each of noise, void discharge, corona discharge, and floating discharge, and performs an operation on the detection data detected by the detection device 100 to determine the identification element After deriving the vector, the derived identification element vector is compared with a vector in the stored cluster of identification element vectors, and the identification element vector having the smallest angle between the two vectors is compared and detected.

In the above configuration, a process for constructing comparison data that is a cluster of previously stored identification element vectors will be described.

In general comparison data, the device is configured to generate the corresponding discharge, and the same discharge is generated several times to calculate the identification element vector for the corresponding partial discharge.

That is, in order to simulate the selected single discharge, a device for generating the selected discharge is installed, and a signal according to the discharge is detected to build a cluster. It is composed of a set of stored identification element vectors).

However, in practice, in the case of partial discharges occurring in the field, several partial discharges may be generated in a complex manner, and the generated partial discharges may be affected by other power devices, or signals may be distorted by reflected waves.

In this way, when the detection data for the complex partial discharge occurring in the actual field is compared with the comparison data stored in advance, it is impossible to accurately diagnose which type of discharge the detected data is.

Accordingly, the cluster composed of identification element vectors stored in advance as comparison data can be utilized as comparison data for accurate diagnosis only when learning is performed with data learned about various partial discharge signals or signal distortions.

In the present invention, the result derived by learning by using learning data obtained by mixing real data detected by simulation and randomly generated fake data is used as comparative data.

Here, the simulated data uses data generated using a Deep Convolutional Generative Adversarial (DCGAN) algorithm.

The DCGAN is a generative adversarial network (GAN) in which a discriminator and a generator are configured with a CNN (Convolutional Neural Network) structure, and is such a deep learning structure to make a function approximating the actual data distribution, and it is utilized in the present invention.

The GAN is an algorithm that trains a model made by deep learning in an adversarial learning method to solve the problem of generation, and generates simulated data similar to real data.

The objective function for the learning method is expressed by Equation 4 below.

Equation 4)

Here, x is real data, and G(z) is simulated data generated by the generator with noise z as input.

In this case, the objective function for D is expressed by Equation 5, and the objective function for G is expressed by Equation 6.

Equation 5)

Equation 6)

Equation 4 is expressed in the range from minus infinity to 0, and from the point of view of the discriminator, x is classified as real (D(x) = 1), and G(z) is simulated (D(Gz)) = 0). It should be classified so that V(D,G) of Equation 5) becomes 0.

On the contrary, the generator classifies x input by the discriminator as fake (D(x)=0) and classifies G(x) as real (D(G(x)=1), so that V(D, G) in Equation 4 to be minus infinity, that is, in Equation 4, the discriminator is maximized and the generator is minimized.

However, the GAN is an algorithm that generates an image by opposing two neural networks, and generates an image by rearranging randomly generated noise when generating an image. An image generated in this way is not well generated due to noise, and when the number of pixels in the image is small, it may be difficult to generate a proper image. Due to this instability, the learned result value deviated considerably from the reference value, and the improved algorithm is DCGAN.

12 is a view showing a process of generating simulated data through DCGAN applied to the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

Referring to FIG. 12 attached, two-dimensional real data (PRPS) is learned using a generator and a discriminator, and two-dimensional simulated data is used through a trained generator of the learned result. is configured to create

The generator generates data (image) to deceive the discriminator, and the discriminator performs a function of discriminating whether the given data (image) is real data or simulated data.

13 shows a configuration table of experimental data including simulated data applied to the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention.

Referring to the attached FIG. 13 , a relatively larger number of simulated data was used than the actual data, and specifically, the simulated data was generated and used in an approximate amount of 1.2 to 3.5 times that of the actual data.

14 is a graph showing a PRPD three-dimensional waveform using actual data of noise (a), void discharge (b), corona discharge (c) and floating electrode discharge (d), and FIG. 15 is noise (a), void discharge (d). The graph of the PRPD three-dimensional waveform of the data obtained by mixing the actual data and simulated data of the discharge (b), corona discharge (c), and floating electrode discharge (d) is shown.

14 and 15, the shape of the PRPD 3D waveform derived using only real data and the shape of the PRPD 3D waveform to be derived using mixed data in which real data and simulated data are mixed are almost similar. Confirmed.

16 is a structure of a Convolutional Neural Networks (CNN) used as an algorithm for determining a discharge type in the diagnostic module of the present invention, and includes two convolutional layers, a pooling layer, a fully connected layer, and an output layer. At this time, the method used in the entire pooling layer is the Max Pooling method, the ReLu function is used as the activation function of each layer, and the Soft-max function is configured as the function of the output layer. In this case, the output is expressed by Equations 7 and 8.

Equation 7)

Here, z means an input value, and if the input value is positive, it is replaced with z, and if the input value is 0 or negative, it is replaced with 0.

Equation 8)

Here, c is the total number of classes (in the present invention, a total of 4 as noise, void, corona, and floating discharge), a _k is the input input to the i-th node of the output layer, y _k is the output of the k-th node (initial convolutional It is the probability that the image entered as the input of the layer has class k).

17 is a table showing the learning accuracy of PRPS-based comparison data applied to the switchboard partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention, and FIG. It is a table for the accuracy of the result, and FIG. 17 (b) is a table for the accuracy of the result learned using mixed data obtained by mixing real data and simulated data. At this time, in the output class of FIG. 17, 1 is noise, 2 is void discharge, 3 is corona discharge, and 4 is floating electrode discharge.

As shown in the attached FIG. 17 , through comparison of the accuracy using only real data and the accuracy of mixed data mixed with real data and simulated data, 86.36% of cases in which simulated data were additionally used overall were 86.36% when only real data was used. It can be seen that the performance is higher than 0.68%.

Among them, it can be seen that noise increased by 2.43%. However, in the case of void discharge and corona discharge, it can be seen that the accuracies are lowered by 3.09% and 3.91%, respectively, but the accuracies in the void discharge and corona discharge are respectively 96.91% and 94.95%, which is relatively high compared to the accuracy of noise. It was confirmed that the accuracy was shown.

18 is a table showing the learning accuracy of PRPD-based comparison data applied to the intelligent switchboard partial discharge monitoring and diagnosis system using ultra-high frequency electrical signals according to the present invention. At this time, in the output class of FIG. 18 , 1 is noise, 2 is void discharge, 3 is corona discharge, and 4 is floating discharge. Comparing FIG. 17A and FIG. 18 , the case of using the PRPD image shows a classification rate of 97.43%, which is about 11.7% higher overall than the case of the PRPS.

Corona discharge, insulator discharge and floating discharge are classified as 100%, but 12 out of 116 noises are misclassified as void discharge.

19 is a table showing the learning accuracy of APRPPD-based comparison data applied to the intelligent switchboard partial discharge monitoring and diagnosis system using ultra-high frequency electrical signals according to the present invention. Comparing FIGS. 18 and 19 , the case using the APPRPD image ( FIG. 19 ) shows a classification rate of 99.29%, which is 1.86% higher overall than the case of the PRPD ( FIG. 18 ).

The diagnosis module 324 diagnoses the type of partial discharge for the cluster derived from the comparison module 323, and provides the diagnosed partial discharge to the HMI for display.

According to the diagnosis unit 320, when the detection data detected by the detection apparatus 100 is input in a state in which a cluster of identification element vectors is stored in advance according to the type of partial discharge, a valid identification element is extracted from the input detection data. And after classifying the pattern, it is possible to diagnose the type of partial discharge with respect to the detection data detected by the detection device 100 by comparing it with the cluster of previously stored identification element vectors.

The output unit 330 transmits the results diagnosed by the diagnosis unit 320 to an HMI such as an external device (server, etc.) or an external terminal (manager communication device, etc.) for display.

According to the present invention, the partial discharge generated in the power facility of the switchboard is detected by a plurality of UHF sensors and can be diagnosed by a remote monitoring device. There is an advantage of automatically recognizing the frequency characteristic of the partial discharge for each characteristic and diagnosing the partial discharge according to the recognized frequency characteristic.

In the above, a preferred embodiment of the switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals according to the present invention has been described, but the present invention is not limited thereto, and within the scope of the claims and the description of the invention and the accompanying drawings. It is possible to carry out various modifications in this, and this also falls within the scope of the present invention.

100: detection device 101: UHF module
103: HFCT module 110: UHF sensor
120: UHF converter 121: amplifier
122: first diode 123: first capacitor
124: second capacitor 125: shunt resistor
130: HFCT sensor 140: HFCT converter
141: first amplifier 142: band pass filter
143: second amplifier 200: signal processing device
210: AD converter 220: composite signal processor
230: temporary memory 240: trigger input device
250: RTC 260: digital signal processor
300: monitoring device 310: signal receiver
320: diagnostic unit 321: identification element extraction module
322: pattern classification module 323: comparison module
324: diagnostic module 330: output unit

Claims (11)

High voltage cable, transformer, GIS (Gas Insulated Switchgear, gas insulated switchgear), a switchgear, a detection device 100 for detecting a partial discharge occurring in any one of the power receiving equipment and the switchboard;
a signal processing device 200 for converting the signal transmitted from the detection device 100 into a digital signal and transmitting the converted signal; and
a monitoring device 300 for analyzing and diagnosing the digital signal transmitted from the signal processing device 200;
consists of,
The detection device 100,
UHF (Ultra High Frequency) module 101 which is installed in the switchboard and detects an electric signal of very high frequency generated by partial discharge in the switchboard using microwaves of 300 MHz to 3 GHz; and
a High Frequency Current Transformer (HFCT) module 103 installed on the ground wire of the switchboard to detect an electrical signal of a high frequency current generated by partial discharge;
including,
The signal processing device 200,
an AD converter 210 for converting each analog signal output from the detection device 100 into a digital signal;
a complex signal processor 220 for sequentially outputting a plurality of digital signals output from the AD converter 210 according to a set logic;
a temporary memory 230 for temporarily storing the digital signal output from the composite signal processor 220;
a trigger input unit 240 for providing a trigger signal;
RTC 250 providing real time time; and
A digital signal processor 260 for transmitting the digital signal of the temporary memory 230 to the monitoring device 300 based on the trigger signal provided from the trigger input unit 240 based on the real time time provided from the RTC 250 . );
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, comprising:
The method according to claim 1,
The UHF module 101,
UHF sensor 110; and
a UHF converter 120 for detecting and sampling a peak signal of the signal detected by the UHF sensor 110;
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, characterized in that it comprises a.
3. The method according to claim 2,
The UHF converter 120,
an amplifier 121 amplifying the signal output from the UHF sensor 110;
a first diode (122) for blocking a low-frequency signal with respect to the signal output from the amplifier (121);
a first capacitor 123 charged with a peak voltage when the signal output from the first diode 122 is a positive pulse signal;
a second capacitor 124 for charging a voltage with respect to a pulse signal between the first diode 122 and the first capacitor 123; and
a shunt resistor 125 connected in parallel with the second capacitor 124 to limit a charging current to the second capacitor 124 and discharge a charging voltage;
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, comprising:
3. The method according to claim 2,
The UHF sensor 110,
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, characterized in that it consists of an M-shaped microstrip structure antenna.
The method according to claim 1,
The HFCT module 103,
HFCT sensor 130; and
HFCT converter 140 for adjusting the signal detected by the HFCT sensor 130;
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, characterized in that it comprises a.
6. The method of claim 5,
The HFCT converter 140,
a first amplifier 141 for amplifying a signal output from the HFCT sensor 130;
a band-pass filter 142 for filtering the signal amplified by the first amplifier 141; and
a second amplifier 143 amplifying the signal output from the band pass filter 142;
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, characterized in that it comprises a.
7. The method of claim 6,
The band pass filter 142,
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, characterized in that it passes the frequency of 2 to 10 MHz.
delete The method according to claim 1,
The monitoring device 300,
a diagnosis unit 320 for diagnosing partial discharge with respect to the digital signal transmitted from the signal processing device 200; and
an output unit 330 for transmitting the diagnosis result of the diagnosis unit 320 to an external device or HMI, which is an external terminal;
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, characterized in that it comprises a.
10. The method of claim 9,
The diagnosis unit 320,
an identification element extraction module 321 for extracting identification elements from the received detection data, which is the digital signal;
a pattern classification module 322 for classifying patterns of identification elements into clusters by using an identification element vector with respect to the identification elements extracted by the identification element extraction module 321;
a comparison module 323 that compares which cluster of the comparison data stored in advance with the detection data through the pattern classification module 322; and
a diagnosis module 324 for diagnosing a type of partial discharge with respect to the detected data with respect to the cluster compared in the comparison module 323;
A switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals, comprising:
11. The method of claim 10,
The comparative data is
It learns with learning data mixed with real data detected by simulation and randomly generated fake data, and the discriminator of the learning is determined using CNN (Convolutional Neural Networks), characterized in that Switchgear partial discharge monitoring and diagnosis system using UHF and HFCT electrical signals.

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