Multi-source partial discharge identification method based on PRPD atlas

Abstract

The invention relates to the technical field of discharge fault detection, in particular to a multi-source partial discharge identification method based on a PRPD atlas, and the method comprises the following steps: reading a sound signal; establishing a signal representation and array directivity function; performing cross-correlation calculation to obtain a horizontal azimuth angle and a pitch angle; obtaining a composite signal representation; a PRPD map is drawn; calculating the phase difference between adjacent peak values, and if the phase difference is within a preset range, judging that discharge exists; carrying out phase synchronization, and superposing and combining every three periods of the synthesized signal; respectively calculating preset characteristic values; and comparing the preset characteristic value with the representative characteristic value of the known discharge type, and if the similarity exceeds a preset threshold value, determining that discharge of the discharge type exists. The method has the beneficial technical effects that the horizontal azimuth angle and the pitch angle of each partial discharge sound source can be obtained, and the discovery and positioning of the partial discharge condition are realized; the discharge type of partial discharge can be identified, and a reference is provided for fault identification and disposal.

Description

Technical Field

[0001] The present invention relates to the technical field of discharge fault detection, and particularly to a multi-source partial discharge recognition method based on a PRPD pattern. Background Art

[0002] Most of the existing technologies identify a single type of partial discharge in an ideal environment, and a small part of them identify the partial discharge type in some noisy environments. The recognition environment is restricted to only allowing one object to generate discharge, and the interference removed by these methods only includes some white noise and some low-frequency band-pass noise. However, if there is also discharge from other objects in the environment, in this case, new features will be generated by the sounds emitted by the two different discharge types, which will greatly reduce the recognition accuracy. And the effective information generated by the discharge of another object also needs to be recognized. If one of the discharge signals is simply eliminated in the mixed signal, the corresponding discharge object cannot be matched. Therefore, it is necessary to study a technology that can identify each discharge position in the case of discharges at multiple positions.

[0003] The prior art discloses a partial discharge type recognition device, which includes a detection circuit and a processing module; the access end of the detection circuit is connected to the switch cabinet shell, and the output end is connected to the processing module; wherein, the detection circuit is used to determine the equivalent impedance of the detection circuit according to the electromagnetic wave generated by the partial discharge of the switch cabinet; the processing module is used to identify the output voltage waveform of the detection circuit, and adjust the equivalent impedance of the detection circuit until the output voltage waveform of the detection circuit reaches the peak value according to the recognition result; the processing module is also used to determine the type of partial discharge of the switch cabinet according to the output voltage of the detection circuit and the frequency of the output voltage waveform when the output voltage waveform reaches the peak value. Although its technical solution can obtain a relatively stable and clear waveform when the waveform of the output voltage reaches the peak value, and can obtain the accurate voltage value of the output voltage and the frequency of the waveform according to this waveform, improving the accuracy of partial discharge type recognition. However, its technical solution cannot solve the discharge recognition in the case of multi-source partial discharge. Summary of the Invention

[0004] The technical problem to be solved by the present invention: There is currently a lack of a technical solution for multi-source discharge recognition in the case of discharges at multiple positions. A multi-source partial discharge recognition method based on a PRPD pattern is proposed, which can realize the recognition of multi-source partial discharge.

[0005] To solve the above technical problem, the present invention adopts the following technical solution: A multi-source partial discharge recognition method based on a PRPD pattern, comprising the following steps: Read the sound signals received by the array of N microphones, and record the number of partial discharges as The horizontal azimuth angle and elevation angle of the partial discharge are respectively recorded as and , ; Establish the signal representation received by N microphones at time t and the array directivity function; Perform cross-correlation calculation on the sound signals received by N microphones to obtain the horizontal azimuth angle of each partial discharge sound source and the elevation angle ; According to the horizontal azimuth angle of each partial discharge sound source and the elevation angle Obtain the synthesized signal representation received by N microphones at time t ; Based on the synthesized signal received by N microphones at time t Draw the PRPD pattern; Calculate the phase difference between adjacent peaks of the synthesized signal. If the phase difference between adjacent peaks is within the preset range, it is determined that there is a discharge. Otherwise, if the phase difference between adjacent peaks is not within the preset range, it is determined that there is no discharge; If there is a discharge, synchronize the phase of the synthesized signal with the power grid signal, and superimpose and combine every three cycles of the synchronized synthesized signal to form new sound signal data ; Calculate respectively the preset characteristic values of the sound signals received by each microphone in the sound signal data ; Compare the preset characteristic values with the representative characteristic values of the known discharge types . If the similarity exceeds the preset threshold, it is determined that there is a discharge of the discharge type . Otherwise, if the similarity does not exceed the preset threshold, it is determined that there is no discharge of the discharge type .

[0006] Preferably, the method for establishing the signal representation received by N microphones at time t includes: ; wherein: ; and to respectively represent the coordinates of N microphones, and is the sound wavelength of the partial discharge, represents white noise, to respectively represent the amplitudes when the sound signals of L partial discharges propagate to the microphone array.

[0007] Preferably, the method for establishing the array directivity function of N microphones includes: ; where represents the distance between the -th microphone and the preset central microphone, represents the wave vector of the sound signal of the -th partial discharge in the focusing direction, represents the wave vector in the incident direction, , , is set to 90°, , and respectively represent the unit vectors of the x-axis, y-axis and z-axis, is the sound wavelength of the partial discharge, is set to 0°.

[0008] Preferably, the method for performing cross-correlation calculation to obtain the horizontal azimuth angle and elevation angle of each partial discharge sound source includes: Select the preset central microphone as the reference microphone, and perform cross-correlation operation on the signal collected by the reference microphone and the signal collected by the -th microphone to be determined to obtain the time delay , then the horizontal azimuth angle , where represents the speed of sound, represents the horizontal distance between the microphone and the reference microphone, and the elevation angle can be expressed as , where represents the vertical distance between the microphone and the reference microphone.

[0009] Preferably, the method for drawing the PRPD map based on the composite signal received by N microphones at time t includes: Calculate , and establish a PRPD map with t as the horizontal axis as the vertical axis, where means adding the elements of each row of to form an N*1 matrix, means adding all the elements of .

[0010] Preferably, the preset characteristic values include discharge symmetry S, signal skewness , signal kurtosis R, signal average value and signal variance value, and compare the preset characteristic values with known discharge types The method for comparing representative characteristic values includes: Performing mRMR operation on multiple said preset characteristic values to obtain the most representative characteristic values, denoted as representative characteristic values; Establishing a discriminant, the discriminant is , where , where is the th representative characteristic value of the discharge signal to be recognized received by the kk - th microphone, is the th representative characteristic value of the known discharge type, is the weight of the th representative characteristic value; If > 0, it is determined that a discharge of the discharge type occurs. On the contrary, if ≤0, it is determined that no discharge of the discharge type occurs.

[0011] Preferably, the method for calculating the discharge symmetry S is: ; The method for calculating the signal skewness is: , where is the mean value of , is the variance of , and the function represents finding the target expected value; The method for calculating the signal kurtosis is: .

[0012] Preferably, the method for calculating the phase difference between adjacent peaks of the composite signal includes: Intercepting the signal of the first four cycles of the composite signal after phase synchronization, and finding the peak value of each cycle signal; Calculating the phase difference between the four peak values. If the phase differences between the peak values are not within the preset range, it is determined that there is no discharge. On the contrary, if there is a phase difference falling within the preset range among the four peak values, it is determined that there is a discharge.

[0013] Preferably, the preset range corresponding to the phase difference is [330°, 390°].

[0014] Preferably, the method for phase synchronization between the synthesized signal and the grid signal includes: using the phase of the first peak as a reference, multiplying the subsequent phases by a scaling factor to shift the second peak to a phase difference of 85° relative to the first peak, thereby completing the phase synchronization between the synthesized signal and the grid signal.

[0015] The beneficial technical effects of the present invention include: by establishing a signal representation and an array directivity function, realizing the functional representation of the microphone array signal, and then performing cross-correlation calculations, the horizontal azimuth angle and elevation angle of each partial discharge sound source can be obtained, realizing the discovery and positioning of partial discharge conditions; by establishing representative eigenvalues and comparing them with known discharge types, the discharge type of partial discharge can be identified, providing a reference for identifying and handling faults.

[0016] Other features and advantages of the present invention will be disclosed in detail in the following specific embodiments and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The following further describes the present invention with reference to the accompanying drawings: Figure 1 It is a schematic flow chart of a multi-source partial discharge identification method according to an embodiment of the present invention.

[0018] Figure 2 It is a schematic flow chart of a method for calculating the phase difference between adjacent peaks of a synthesized signal according to an embodiment of the present invention.

[0019] Figure 3 It is a schematic diagram of a PRPD pattern of suspension discharge according to an embodiment of the present invention.

[0020] Figure 4 It is a schematic diagram of a PRPD pattern of corona discharge according to an embodiment of the present invention.

[0021] Figure 5 It is a schematic diagram of a PRPD pattern of surface discharge according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following explains and illustrates the technical solutions of the embodiments of the present invention with reference to the accompanying drawings of the embodiments of the present invention. However, the following embodiments are only the preferred embodiments of the present invention and not all of them. Based on the embodiments in the embodiments, other embodiments obtained by those skilled in the art without creative efforts all fall within the protection scope of the present invention.

[0023] In the following description, terms such as "inner", "outer", "upper", "lower", "left", "right", etc., indicating orientation or positional relationships are only for convenience in describing the embodiments and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0024] A multi-source partial discharge recognition method based on PRPD pattern, please refer to the appendix Figure 1 , including the following steps: Step A01) Read the sound signals received by the array of N microphones. The number of partial discharges is denoted as , and the horizontal azimuth angle and elevation angle of the partial discharge are respectively denoted as and , ; Step A02) Establish the signal representation received by N microphones at time t and the array directivity function; Step A03) Perform cross-correlation calculation on the sound signals received by N microphones to obtain the horizontal azimuth angle and elevation angle of each partial discharge sound source; Step A04) According to the horizontal azimuth angle and elevation angle of each partial discharge sound source, obtain the synthetic signal representation received by N microphones at time t; Step A05) Draw the PRPD pattern based on the synthetic signal received by N microphones at time t; Step A06) Calculate the phase difference between adjacent peaks of the synthetic signal. If the phase difference between adjacent peaks is within the preset range, it is determined that there is a discharge. Otherwise, if the phase difference between adjacent peaks is not within the preset range, it is determined that there is no discharge; Step A07) If there is a discharge, synchronize the phase of the synthetic signal with the grid signal, and superimpose and merge every three cycles of the synchronized synthetic signal to form new sound signal data ; Step A08) Calculate the preset characteristic values of the sound signals received by each microphone in the sound signal data respectively; Step A09) Compare the preset characteristic values with the representative characteristic values of the known discharge type . If the similarity exceeds the preset threshold, it is determined that there is a discharge of the discharge type . Otherwise, if the similarity does not exceed the preset threshold, it is determined that there is no discharge of the discharge type .

[0025] Among them, the method for establishing the signal representation received by N microphones at time t includes: ; Among them: ; , to respectively represent the coordinates of N microphones. , is the sound wavelength of partial discharge. represents white noise. to respectively represent the amplitudes when the sound signals of L partial discharges propagate to the microphone array.

[0026] On the other hand, the method provided in this embodiment for establishing the array directivity function of N microphones includes: ; where represents the distance between the th microphone and the preset central microphone. represents the wave vector of the sound signal of the th partial discharge in the focused pointing direction. represents the wave vector in the incident direction. , , is set to 90°, , and respectively represent the unit vectors of the x-axis, y-axis and z-axis. is the sound wavelength of partial discharge. is set to 0°.

[0027] The angle of the incident direction being set to 90° means that the microphone array only receives sounds directly in front.

[0028] The method for performing cross-correlation calculation to obtain the horizontal azimuth angle and elevation angle of each partial discharge sound source includes: Select the preset central microphone as the reference microphone, and perform cross-correlation operation on the signal collected by the reference microphone and the signal collected by the th microphone to be solved to obtain the time delay , then the horizontal azimuth angle , where represents the sound speed, represents the horizontal distance between the microphone and the reference microphone, and the elevation angle can be expressed as , where represents the vertical distance between the microphone and the reference microphone.

[0029] According to the composite signal received by N microphones at time t The method for drawing a PRPD spectrum includes: Calculating , using t as the horizontal axis to establish a PRPD spectrum with as the vertical axis, where means adding up each row of elements to form an N*1 matrix, and means adding up all the elements of means Adding up all the elements of.

[0030] The preset characteristic values include discharge symmetry S, signal skewness , signal kurtosis R, signal average value, and signal variance value. The method for comparing the preset characteristic values with the representative characteristic values of known discharge types includes: Performing mRMR operation on multiple said preset characteristic values to obtain preset The mRMR operation (Max-Relevance and Min-Redundancy) refers to the maximum correlation and minimum redundancy algorithm. It can find a set of features in the original feature set that has the maximum correlation (Max-Relevance) with the final output result, but the minimum correlation between features (Min-Redundancy). The calculation process of the mRMR operation belongs to the prior art. As long as the features associated with the results are provided, the maximum correlation and minimum redundancy feature ranking result can be obtained according to the publicly disclosed calculation steps. In this embodiment, the first 4 features are selected as the representative characteristic values. The most representative characteristic values, denoted as representative characteristic values; Establishing a discriminant, the discriminant is , where , where is the kk-th representative characteristic value of the discharge signal to be recognized received by the kk-th microphone, is the k-th representative characteristic value of the known discharge type , is the k-th representative characteristic value, is the weight of the k-th representative characteristic value; If >0, it is determined that a discharge of the discharge type has occurred. Conversely, if ≤0, it is determined that no discharge of the discharge type has occurred.

[0031] The method for calculating the discharge symmetry S is:

[0032] ; ; The method for calculating the signal skewness is: , where is the mean value of , and is the variance of . The function represents finding the target expected value; The method for calculating the signal kurtosis is as follows: .

[0033] Please refer to Appendix Figure 2 . The method for calculating the phase difference between adjacent peaks of the synthesized signal includes: Step B01) Intercept the signals of the first four cycles of the synthesized signal after phase synchronization, and find the peak value of each cycle signal; Step B02) Calculate the phase differences between the four peak values. If the phase differences between the peak values are not within the preset range, it is determined that there is no discharge. On the contrary, if there is a phase difference among the four peak values that falls within the preset range, it is determined that there is a discharge.

[0034] The preset range corresponding to the phase difference is [330°, 390°]. If the phase difference between the maximum peak values of adjacent cycles is not between 330 - 390 degrees, it is considered that there is no discharge within these four cycles, and the next four-cycle signal is intercepted.

[0035] The method for phase-synchronizing the synthesized signal and the power grid signal includes: taking the phase of the first peak as a reference, multiplying the subsequent phases by a scaling factor so that the second peak moves to a phase difference of 85° relative to the first peak, that is, the phase synchronization of the synthesized signal and the power grid signal is completed. According to the working frequency of the State Grid, which is 50 Hz, the received signal can be regarded as one working cycle every 20 ms. The received acoustic signal and the power grid signal are phase-synchronized according to the working cycle. If the phase difference between the maximum peak values of adjacent cycles is within 330 - 390 degrees, it is considered that there is a discharge signal within the four cycles. Then, calculate the phase of the peak value of the second cycle, and the data of the first cycle is used as the phase buffer area. Move the phase of the second peak to the 85-degree phase position of its cycle. After the above steps, it is considered that the phases of the power grid signal and the acoustic signal are synchronized or differ by an integer number of cycles.

[0036] This embodiment provides examples of several discharge types, including floating discharge, corona discharge, and surface discharge. Floating discharge means that the loop has no effective grounding. Since there is no voltage zero point in floating discharge, it will cause voltage instability and may lead to discharge phenomena at weak insulation points. The PRPD pattern of floating discharge is as Figure 3As shown. Corona discharge refers to the local self-sustaining discharge of a gas medium in a non-uniform electric field and is the most common form of gas discharge. Near the tip electrode with a very small radius of curvature, since the local electric field strength exceeds the ionization field strength of the gas, the gas is ionized and excited, thus corona discharge occurs. When corona occurs, a glow can be seen around the electrode, accompanied by a hissing sound. Corona discharge can be a relatively stable form of discharge or an early development stage in the breakdown process of a non-uniform electric field gap. The PRPD pattern of corona discharge is as Figure 4 shown.

[0037] Surface discharge refers to the discharge phenomenon along the interface of different aggregated state dielectrics. Usually, more frequently, it is the discharge along the surface of a solid dielectric in a gas or liquid dielectric. No charged conductor can be suspended in the atmosphere and must be suspended or supported by a solid insulation device. When a charged conductor needs to pass through a wall or the oil tank of an electrical equipment, a wall bushing or an equipment bushing is also used for fixation and insulation. These solid insulation devices not only play a fixing role mechanically but also play an insulating role electrically. They are all surrounded by a gas medium, and often one electrode is connected to a high voltage and the other electrode is grounded. There are two possibilities for the loss of the insulation function between the two electrodes: one is the breakdown of the solid dielectric itself, and the other is the flashover along the surface of the solid dielectric. The PRPD pattern of surface discharge is as Figure 5 shown.

[0038] Under laboratory conditions, a local discharge simulation device is used to simulate the corresponding discharge types. An array of N microphones is used to collect sound signals, and the representative characteristic values of the sound signals are calculated. The actually collected sound signals are compared with the representative characteristic values of known discharge types to obtain the matching discharge types, realizing the identification of discharge types.

[0039] The beneficial technical effects of this embodiment include: By establishing a signal representation and an array directivity function, the functional representation of the microphone array signal is realized, and then cross-correlation calculation is performed, so that the horizontal azimuth angle and elevation angle of each local discharge sound source can be obtained, realizing the discovery and positioning of local discharge conditions; By establishing representative characteristic values and comparing them with known discharge types, the discharge types of local discharges can be identified, providing a reference for identifying and disposing of faults.

[0040] As described above, only the specific embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that the present invention includes but is not limited to the content described in the drawings and the above specific embodiments. Any modification that does not deviate from the functional and structural principles of the present invention will be included in the scope of the claims.

Claims

1. A multi-source partial discharge recognition method based on PRPD pattern, characterized in that: It includes the following steps: Read the sound signals received by an array of N microphones, and record the number of partial discharges as , and record the horizontal azimuth and elevation angle of the partial discharge as and , ; Establish the signal representation received by N microphones at time t and the array directivity function; Perform cross-correlation calculations on the sound signals received by N microphones to obtain the horizontal azimuth angle of each partial discharge sound source and the pitch angle ; According to the horizontal azimuth angle of each partial discharge sound source and the pitch angle obtain the synthetic signal representation received by N microphones at time t ; Based on the composite signal received by N microphones at time t Draw a PRPD map; Calculate the phase difference between adjacent peaks of the synthesized signal. If the phase difference between adjacent peaks is within a preset range, it is determined that there is a discharge. Conversely, if the phase difference between adjacent peaks is not within the preset range, it is determined that there is no discharge; If there is a discharge, the synthesized signal is phase-synchronized with the grid signal, and every three cycles of the synchronized synthesized signal are superimposed and combined to form new sound signal data ; Calculate the preset feature values of the sound signals received by each microphone in the sound signal data respectively ; Compare the preset characteristic value with the representative characteristic value of the known discharge type If the similarity exceeds the preset threshold, it is determined that there is a discharge of the discharge type Otherwise, if the similarity does not exceed the preset threshold, it is determined that there is no discharge of the discharge type .

2. The multi-source partial discharge recognition method based on a PRPD pattern according to claim 1, characterized in that Method for representing signals received by N microphones at time t comprises: ； Wherein: ； , to respectively represent the coordinates of N microphones, , is the sound wavelength of partial discharge, represents white noise, to respectively represent the amplitudes when the sound signals of L partial discharges propagate to the microphone array.

3. A multi-source partial discharge recognition method based on PRPD pattern according to claim 1, characterized in that, The method for establishing the array directivity function of N microphones includes: ； wherein represents the distance between the th microphone and a preset central microphone, represents the wave vector of the th partial discharge sound signal in the focusing direction, represents the wave vector of the incident direction, , , is set to 90°, , and respectively represent the unit vectors of the x-axis, y-axis and z-axis, is the sound wavelength of the partial discharge, is set to 0°.

4. A multi-source partial discharge recognition method based on a PRPD pattern according to claim 1, characterized in that, Perform cross-correlation calculations to obtain the horizontal azimuth angle of each partial discharge sound source and the pitch angle The method includes: Select the preset central microphone as the reference microphone, and perform cross-correlation operation on the signal collected by the reference microphone and the signal collected by the th microphone to be determined to calculate the time delay . Then the horizontal azimuth , where represents the speed of sound, represents the horizontal distance between the microphone and the reference microphone, and the pitch angle can be expressed as , where represents the vertical distance between the microphone and the reference microphone.

5. A multi-source partial discharge recognition method based on a PRPD pattern according to claim 1, characterized in that According to the synthesized signal received by N microphones at time t The method for drawing a PRPD map includes: Calculate With t as the horizontal axis Establish a PRPD spectrum, where Indicates adding The elements of each row are added to form an N*1 matrix Indicates adding All elements of 6. A multi-source partial discharge recognition method based on a PRPD pattern according to claim 1, characterized in that The preset characteristic values include discharge symmetry S, signal skewness , signal kurtosis R, signal mean value, and signal variance value. The method of comparing the preset characteristic values with the representative characteristic values of known discharge types includes: Perform mRMR operations on multiple said preset eigenvalue to obtain the most representative eigenvalues, denoted as representative eigenvalues; Establish a discriminant, and the discriminant is , where , where is the th representative eigenvalue of the discharge signal to be recognized received by the kkth microphone, is the th th representative eigenvalue of the known discharge type, is the weight of the th representative eigenvalue; If > 0, it is determined that a discharge of the discharge type occurs. Conversely, if ≤ 0, it is determined that a discharge of the discharge type does not occur.

7. A multi-source partial discharge recognition method based on a PRPD pattern according to claim 6, characterized in that The method for calculating the discharge symmetry S is as follows: ; Method for calculating signal skewness is as follows: , where is the mean value of , is the variance of , and the function represents finding the target expected value; Method for calculating signal kurtosis is as follows: .

8. A multi-source partial discharge recognition method based on a PRPD pattern according to claim 1, characterized in that The method for calculating the phase difference between adjacent peaks of the synthesized signal includes: Intercept the signal of the first four cycles of the synthesized signal after phase synchronization, and find the peak value of each cycle signal; Calculate the phase difference between the four peak values. If the phase differences between the peak values are not within the preset range, it is determined that there is no discharge. Conversely, if there is a phase difference among the four peak values that falls within the preset range, it is determined that there is a discharge.

9. A multi-source partial discharge recognition method based on a PRPD pattern according to claim 1, characterized in that The preset range corresponding to the phase difference is [330°, 390°].

10. A multi-source partial discharge recognition method based on a PRPD pattern according to claim 8, characterized in that The method for phase synchronization of the synthesized signal and the power grid signal includes: taking the phase of the first peak as a reference, multiplying the subsequent phases by a scaling factor so that the second peak moves to a phase difference of 85° relative to the first peak, that is, the phase synchronization of the synthesized signal and the power grid signal is completed.

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