Ultrahigh frequency partial discharge multi-source separation method and system

Abstract

The invention discloses an ultrahigh-frequency partial discharge multi-source separation method and system. The method comprises the steps that ultrahigh-frequency partial discharge pulse signals are collected through double sensors; respectively calculating ultrahigh frequency partial discharge pulse signal characteristics received by the two sensors; constructing a characteristic spectrum based on the calculated ultrahigh frequency partial discharge pulse signal characteristics; and judging signal-to-noise ratio information in the characteristic spectrum, and separating a noise signal from an effective partial discharge signal to determine a discharge type. According to the invention, through the extraction of the time difference of arrival and the signal-to-noise ratio, visual comparison, determination and screening, the partial discharge signal can be effectively separated from the noise; signals from different partial discharge sources can be identified and separated, and the accuracy of signal identification is improved; pRPD atlas statistics and type identification can be carried out on the separated signals, and more detailed data support is provided for fault diagnosis; positioning can be carried out respectively, so that the specific position of the fault can be determined; the method is suitable for actual power equipment monitoring, and is helpful for improving the reliability and safety of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of discharge detection, and in particular to a method and system for separating multi-sources of ultra-high frequency partial discharge. Background Art

[0002] Partial discharge (PD) is an early sign of insulation aging and failure of power equipment. Traditional PD detection methods mainly rely on a single sensor for signal acquisition, which has shown certain effects in simple environments. However, with the increasing complexity of power systems, the limitations of single sensors have gradually emerged, especially in complex environments, where single sensors are easily interfered by multiple signal sources, resulting in difficulty in signal recognition, which seriously affects the accuracy and reliability of detection. In order to meet this challenge, multi-sensor collaborative acquisition technology has emerged. This technology uses multiple sensors to work simultaneously and collect signals from different angles and positions, which can greatly improve the accuracy and anti-interference ability of signal acquisition.

[0003] Although multi-sensor collaborative acquisition technology has made significant progress in improving signal acquisition quality and anti-interference capabilities, there are still some problems in practical applications. First, when multiple sensors work simultaneously, partial discharge signals from different locations or devices may be collected. These signals may overlap in time and frequency, making the separation of signal sources extremely complicated. Second, when processing multi-source signals, existing signal processing algorithms not only require a large amount of computing resources, but also significantly increase the complexity and running time of the algorithm, which greatly limits its application in real-time monitoring systems.

[0004] Therefore, how to efficiently separate and process multi-source signals and reduce computing resource consumption has become a key issue that needs to be urgently solved in the current multi-sensor partial discharge detection technology. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for separating multiple sources of ultra-high frequency partial discharge to solve the problem that signals may overlap in time and frequency, making separation of signal sources complicated, requiring a large amount of computing resources and time, and resulting in inaccurate separation.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for separating multiple sources of ultra-high frequency partial discharge, comprising:

[0009] Collect ultra-high frequency partial discharge pulse signals through dual sensors;

[0010] Calculate the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors respectively;

[0011] Construct a characteristic spectrum based on the calculated UHF partial discharge pulse signal characteristics;

[0012] The signal-to-noise ratio information in the characteristic spectrum is judged to separate the noise signal and the effective partial discharge signal to determine the discharge type.

[0013] As a preferred solution of the UHF partial discharge multi-source separation method of the present invention, wherein: the separately calculating the characteristics of the UHF partial discharge pulse signals received by two sensors includes: obtaining and calculating the pulse arrival time difference.

[0014] As a preferred solution of the method for separating multiple sources of ultra-high frequency partial discharge according to the present invention, the calculation of the pulse arrival time difference specifically includes:

[0015] Get each pulse signal;

[0016] For the same pulse signal, locate the wave head position received by two sensors;

[0017] Based on the wave head position, obtaining a first receiving time and a second receiving time;

[0018] The difference between the first receiving time and the second receiving time is calculated to obtain the pulse arrival time difference.

[0019] As a preferred solution of the method for separating ultra-high frequency partial discharge multiple sources according to the present invention, the step of respectively calculating the characteristics of ultra-high frequency partial discharge pulse signals received by two sensors further comprises: obtaining a signal-to-noise ratio of the pulse signal;

[0020] The signal-to-noise ratio is defined as the ratio of the amplitude of the peak value P to the minimum value N.

[0021] For the same pulse signal, the pulse received by the two sensors with a larger signal-to-noise ratio is taken as the signal-to-noise ratio feature.

[0022] As a preferred solution of the method for separating multiple sources of ultra-high frequency partial discharge according to the present invention, the method of constructing a characteristic spectrum based on the calculated ultra-high frequency partial discharge pulse signal characteristics comprises:

[0023] Obtain pulse arrival time difference and signal-to-noise ratio characteristics;

[0024] A two-dimensional feature map is constructed with the pulse arrival time difference as the horizontal axis and the signal-to-noise ratio feature as the vertical axis.

[0025] As a preferred solution of the method for separating multiple sources of ultra-high frequency partial discharge according to the present invention, the method of judging the signal-to-noise ratio information in the characteristic spectrum and separating the noise signal and the effective partial discharge signal comprises:

[0026] Setting a first signal-to-noise ratio threshold;

[0027] comparing the signal-to-noise ratio of each pulse signal with a first threshold;

[0028] If it is lower than the first threshold, it is determined to be a noise signal and filtered;

[0029] If it is greater than or equal to the first threshold, it is determined to be a valid partial discharge signal and is retained.

[0030] As a preferred solution of the method for separating multiple sources of ultra-high frequency partial discharge according to the present invention, the method for determining the discharge type includes:

[0031] Obtain effective partial discharge signals and calculate the corresponding PRPD spectrum;

[0032] By comparing the PRPD spectrum with the known defect spectrum, the type of partial discharge defect can be determined.

[0033] In a second aspect, the present invention provides a UHF partial discharge multi-source separation system, comprising:

[0034] An acquisition module, used for acquiring ultra-high frequency partial discharge pulse signals through dual sensors;

[0035] An acquisition module, used for respectively calculating the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors;

[0036] A feature map construction module, used to construct a feature map based on the calculated ultra-high frequency partial discharge pulse signal characteristics;

[0037] The separation and judgment module is used to judge the signal-to-noise ratio information in the characteristic spectrum, separate the noise signal and the effective partial discharge signal, so as to determine the discharge type.

[0038] In a third aspect, the present invention provides an electronic device, comprising:

[0039] Memory and processor;

[0040] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the ultra-high frequency partial discharge multi-source separation method are implemented.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the ultra-high frequency partial discharge multi-source separation method.

[0042] Compared with the prior art, the present invention has the following beneficial effects: the present invention can effectively separate partial discharge signals from noise by extracting arrival time difference and signal-to-noise ratio, intuitively comparing, determining and screening; it can identify and separate signals from different partial discharge sources, thereby improving the accuracy of signal identification; through different feature combinations, it can be optimized for different noise environments and signal characteristics, thereby improving the separation effect; the separated signals can be used for PRPD spectrum statistics and type identification, thereby providing more detailed data support for fault diagnosis; the separated signals can also be located separately, thereby helping to determine the specific location of the fault; and the method is simple to operate, is suitable for actual power equipment monitoring, and helps to improve the reliability and safety of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 A textual schematic diagram of the overall process of the ultra-high frequency partial discharge multi-source separation method according to an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the overall application structure of the ultra-high frequency partial discharge multi-source separation method according to an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of the partial discharge PRPD of sensor A in the ultra-high frequency partial discharge multi-source separation method according to an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of a partial discharge PRPD of sensor B in a method for separating multiple sources of ultra-high frequency partial discharge according to an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the arrival time difference of pulses from two sensors in a method for separating multiple sources of ultra-high frequency partial discharge according to an embodiment of the present invention;

[0049] Figure 6 A schematic diagram of a pulse waveform of a partial discharge signal in a method for separating multiple sources of ultra-high frequency partial discharge according to an embodiment of the present invention;

[0050] Figure 7 A schematic diagram of the pulse waveforms of most noise signals in the ultra-high frequency partial discharge multi-source separation method according to an embodiment of the present invention;

[0051] Figure 8 A schematic diagram of a noise signal with a large signal-to-noise ratio in a method for separating multiple sources of ultra-high frequency partial discharge according to an embodiment of the present invention;

[0052] Fig. 9 A schematic diagram of a demonstration of an arrival time difference experiment in a method for separating multiple sources of ultra-high frequency partial discharge according to an embodiment of the present invention;

[0053] Fig.10 Schematic diagram of the characteristic spectrum of arrival time difference-signal-to-noise ratio (without introducing signal-to-noise ratio) of pulse signals in the ultra-high frequency partial discharge multi-source separation method according to one embodiment of the present invention

[0054] Fig.11 A schematic diagram of a characteristic spectrum of arrival time difference-signal-to-noise ratio of a pulse signal under noise interference in a method for separating multiple sources of ultra-high frequency partial discharge according to an embodiment of the present invention;

[0055] Fig.12 It is a schematic diagram of a pulse signal arrival time difference characteristic spectrum introducing a pulse signal-to-noise ratio in a method for ultra-high frequency partial discharge multi-source separation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0057] Example 1

[0058] Reference Figure 1-Figure 4 , as an embodiment of the present invention, provides a method for separating multiple sources of ultra-high frequency partial discharge, comprising:

[0059] S100: Collects ultra-high frequency partial discharge pulse signals through dual sensors;

[0060] S200: respectively calculating the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors;

[0061] S300: constructing a characteristic spectrum based on the calculated ultra-high frequency partial discharge pulse signal characteristics;

[0062] S400: judging the signal-to-noise ratio information in the characteristic spectrum, separating the noise signal and the effective partial discharge signal, so as to determine the discharge type.

[0063] It should be noted that if Figure 2 As shown in the figure, sensor A and sensor B can simultaneously receive the partial discharge signal from the blue part inside the GIS, the partial discharge signal from the yellow part, and the interference signal from the outside of the GIS. Figure 3 and Figure 4 These are the PRPD spectra of sensors A and B, respectively. There are multiple partial discharge sources in them, making it impossible to identify the specific partial discharge type.

[0064] In order to separate the three types of signals from different sources, through the above steps S100-S400, two UHF sensors are used to simultaneously receive partial discharge signals to enhance the signal recognition capability; the features of the pulse signals synchronously acquired by the two sensors are extracted and separated in the feature space.

[0065] Example 2

[0066] Reference Figure 1 and Figure 4-Figure 12 , which is an embodiment of the present invention, provides a method for separating multiple sources of ultra-high frequency partial discharge based on the above embodiments.

[0067] In the implementation manner of the present application, in step S100, ultra-high frequency partial discharge pulse signals are collected by using dual sensors, wherein the positions of the two sensors are determined according to the partial discharge source.

[0068] If the location of the PD source is not determined at the beginning, the sensors are randomly arranged;

[0069] If the time difference between the signals received by sensor A and sensor B is fixed, it means that the signal source is on one side of sensor A and sensor B, and the fixed spacing is the distance between AB;

[0070] If sensor A and sensor B detect that the time difference of signal arrival is different, it means that the signal source is between AB. Then the signal source is further investigated, the sensor position is continuously adjusted, and the distance is determined.

[0071] In the implementation manner of the present application, the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors are respectively calculated in step S200, including: obtaining the pulse arrival time difference.

[0072] It should be noted that the pulse arrival time difference (δt) is the time difference between the pulses arriving at two sensors, which is used to distinguish different signal sources.

[0073] In the implementation manner of the present application, the pulse arrival time difference is calculated in step S200, which specifically includes the following steps A1-A4:

[0074] A1: Get each pulse signal;

[0075] A2: For the same pulse signal, locate the wave head position received by two sensors;

[0076] A3: Based on the wave head position, obtain the first receiving time and the second receiving time;

[0077] A4: Calculate the difference between the first receiving time and the second receiving time to obtain the pulse arrival time difference.

[0078] Specifically, the above calculation method can be expressed as:

[0079] δt=T A –T B =(Start(T A )-Start(T B ))*0.2*NS

[0080] It should be noted that Figure 4 The blue and green colors in the figure are the original waveforms of the same partial discharge pulse signal received by two UHF sensors. Find the position of the wave head of the original signal. The wave head of the two is the time when the sensor receives the pulse. Then subtracting the two is to calculate the time difference of this pulse reaching the two sensors.

[0081] like Figure 5 As shown in the figure, it is a schematic diagram of the arrival time difference of the pulses of the two sensors. When the pulse arrives at sensor A first, the time difference δt<0, otherwise δt>0. For a certain partial discharge source, δt is a nearly fixed value. Different partial discharge sources have different δt. For noise, δt is a random value. Therefore, this feature has a certain signal separation ability. However, this feature cannot be used alone for separation, because the distribution of this feature of the noise signal may be very wide, thus covering the distribution of this feature of the partial discharge signal, resulting in the inability to separate the partial discharge.

[0082] In the implementation manner of the present application, the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors are calculated in step S200, and further include: obtaining a signal-to-noise ratio of the pulse signal;

[0083] The signal-to-noise ratio is defined as the ratio of the amplitude of the peak value P to the minimum value N.

[0084] For the same pulse signal, the pulse received by the two sensors with a larger signal-to-noise ratio is taken as the signal-to-noise ratio feature.

[0085] It should be noted that the pulse signal-to-noise ratio is defined as the ratio of the pulse peak value to the minimum value, which is used to distinguish partial discharge signals from noise.

[0086] It should also be noted that if Figure 6 As shown in Figure 2, in general, the signal-to-noise ratio of the partial discharge signal pulse is significantly greater than that of the noise pulse; Figure 7 As shown in the figure, for most noise signal pulse waveforms, the peak value and the background noise are not much different. The signal-to-noise ratio of different local discharge source pulses will also be different, which can be used as a feature for separation. Therefore, this feature has a certain signal separation ability. However, this feature cannot be used alone for separation, because there may be Figure 8 The noise signal similar to carrier amplitude modulation has a large signal-to-noise ratio and may be confused with the partial discharge signal. In summary, the time difference and signal-to-noise ratio are combined in feature acquisition.

[0087] For example, Fig. 9 As shown, the two UHF sensor devices are 2.6 meters long and connected to the test equipment. The sampling rate of the test equipment can be 5GHz and 20GHz. The speed of light is 30cm / nanosecond. At a sampling rate of 5GHz, combined with the computing performance of the device, the time interval between the sampling points of the original pulse waveform is 0.2 nanoseconds, that is, the distance between each pulse is 0.2 nanoseconds, so the speed of light can propagate 6cm within a range of 0.2 nanoseconds.

[0088] like Fig. 9 As shown, suppose a partial discharge source is placed at the position of the yellow five-pointed star shown in the figure above. Continuous partial discharge pulses are released at this position. The two UHF sensors find the matching pulse signal through the time difference when receiving the partial discharge pulse. By calculating the time difference between the two pulses and the sensor position, the position of the pulse distance center point can be located by combining the above light propagation speed and the sampling rate of the device.

[0089] It should be noted that if Fig.10 As shown in the figure, in order to conveniently demonstrate that the noise data is filtered out by the custom script, a pulse signal generator is placed at the position of the yellow five-pointed star. The horizontal axis is the time difference between the pulse reaching the sensors on both sides (left minus right), and the red line is the normal distribution of the pulse time difference, μ = 3.15 nanoseconds. Considering that the speed of light is 1 nanosecond and runs 30cm, 3.15 nanoseconds runs 94.5cm, which means that the pulse position is about 94.5cm to the right of the center point. Combined with the actual location of the pulse sensor, it shows that the result of locating the partial discharge source by the pulse time difference is consistent with the actual situation.

[0090] But considering that there are a lot of noises on site, such as Figure 7 and Figure 8 The two noises make it difficult to calculate the wave head. Figure 7The wave head of the calculated noise data is close to 0. Since the wave head of the noise data is very random, the problems that may arise are as follows: Fig.11 As shown in Figure 1, the noise signal will cover the real PD pulse.

[0091] In the embodiment of the present application, in step S300, constructing a characteristic spectrum based on the calculated ultra-high frequency partial discharge pulse signal characteristics includes:

[0092] Obtain pulse arrival time difference and signal-to-noise ratio characteristics;

[0093] A two-dimensional feature map is constructed with the pulse arrival time difference as the horizontal axis and the signal-to-noise ratio feature as the vertical axis.

[0094] For example, Fig.12 As shown, the y-axis is the signal-to-noise ratio of the pulse, and the x-axis is the pulse arrival time difference. The noise signal is the most widely distributed at the bottom, and the most distributed at the middle point. By observing the y-axis, we can find that there are multiple local discharge categories.

[0095] In the embodiment of the present application, the signal-to-noise ratio information in the characteristic spectrum is judged in step S400 to separate the noise signal and the effective partial discharge signal, including:

[0096] Setting a first signal-to-noise ratio threshold;

[0097] comparing the signal-to-noise ratio of each pulse signal with a first threshold;

[0098] If it is lower than the first threshold, it is determined to be a noise signal and filtered;

[0099] If it is greater than or equal to the first threshold, it is determined to be a valid partial discharge signal and is retained.

[0100] Specifically, the first threshold can be 3dB to 6dB, which is suitable for environments with low background noise and can detect weaker partial discharge signals. It can also be a medium range: 6dB to 12dB, which is suitable for general environments and balances detection sensitivity and noise suppression. Or it can be a high range: 12dB to 20dB, which is suitable for environments with high background noise and ensures that the detected signal has high reliability.

[0101] For example, Fig.12 , use the time difference positioning method to collect the signals of the two sensors, intercept a certain point to obtain the time difference and signal-to-noise ratio (SNR) value, take the maximum value of SNR, and then get a set of data. Use the time difference as the horizontal axis and the signal-to-noise ratio as the vertical axis to obtain Fig.12 , because the local discharge signal is the same as the distance between the two sensors during the signal acquisition process, the time difference is fixed, that is, the horizontal axis is the same, but the vertical axis is different. Fig.12The vertical line in the middle represents the local discharge source signal, and the other horizontal axis data are noise, thereby separating the local discharge source and noise.

[0102] In another embodiment, the signal-to-noise ratio information in the characteristic spectrum is judged in step S400 to separate the noise signal and the effective partial discharge signal. Fig.12 The information shown uses a clustering algorithm to separate the noise signal and different partial discharge signals.

[0103] Specifically, when processing signal analysis, especially feature maps in partial discharge detection, clustering is used to identify and separate different types of signals. Through clustering algorithms (such as K-means, DBSCAN, etc.), noise signals can be distinguished from different partial discharge signals based on the features in the feature map (such as amplitude, phase, frequency, etc.). It can automatically find natural groupings in the data so that the signals in the same group are as similar as possible, while the signals between different groups are quite different.

[0104] In the implementation manner of the present application, determining the discharge type in step S400 includes:

[0105] Obtain effective partial discharge signals and calculate the corresponding PRPD spectrum;

[0106] By comparing the PRPD spectrum with the known defect spectrum, the type of partial discharge defect can be determined.

[0107] For example, a pulse signal with a pulse signal-to-noise ratio of about 0.5 (log in the calculation) and an arrival time of about -3ns can be found, and its PRPD spectrum can be obtained through the above steps (because each pulse signal not only records the original waveform, but also records characteristic data such as phase and sampling rate).

[0108] It should be noted that once the separation of noise signals and different types of partial discharge signals is completed, these features can be further analyzed to construct a PRPD (Phase Resolved Partial Discharge) spectrum. The PRPD spectrum can show the relationship between the discharge signal and the power supply voltage phase, which helps to identify the mode and type of discharge, thereby providing a basis for fault diagnosis. In this way, potential defects inside electrical equipment can be effectively identified and located, which is of great significance for preventive maintenance.

[0109] Example 3

[0110] The above is a schematic scheme of a method for separating multiple sources of ultra-high frequency partial discharge in this embodiment. It should be noted that the technical scheme of the system for separating multiple sources of ultra-high frequency partial discharge is the same as the technical scheme of the method for separating multiple sources of ultra-high frequency partial discharge in this embodiment. For details not described in detail in the technical scheme of the system for separating multiple sources of ultra-high frequency partial discharge in this embodiment, please refer to the description of the technical scheme of the method for separating multiple sources of ultra-high frequency partial discharge in this embodiment.

[0111] This embodiment also provides a system for ultra-high frequency partial discharge multi-source separation method, including:

[0112] An acquisition module, used for acquiring ultra-high frequency partial discharge pulse signals through dual sensors;

[0113] An acquisition module, used for respectively calculating the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors;

[0114] A feature map construction module, used to construct a feature map based on the calculated ultra-high frequency partial discharge pulse signal characteristics;

[0115] The separation and judgment module is used to judge the signal-to-noise ratio information in the characteristic spectrum, separate the noise signal and the effective partial discharge signal, so as to determine the discharge type.

[0116] This embodiment also provides an electronic device suitable for ultra-high frequency partial discharge multi-source separation, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the ultra-high frequency partial discharge multi-source separation method proposed in the above embodiment.

[0117] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for separating multi-sources of ultra-high frequency partial discharge proposed in the above embodiment is implemented.

[0118] The storage medium proposed in this embodiment and the method for realizing ultra-high frequency partial discharge multi-source separation proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0119] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for separating ultra-high frequency partial discharge multiple sources, characterized in that: include: Collect ultra-high frequency partial discharge pulse signals through dual sensors; Calculate the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors respectively; Construct a characteristic spectrum based on the calculated UHF partial discharge pulse signal characteristics; The signal-to-noise ratio information in the characteristic spectrum is judged to separate the noise signal and the effective partial discharge signal to determine the discharge type.

2. The method for separating ultra-high frequency partial discharge from multiple sources according to claim 1, characterized in that: The method further includes respectively calculating the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors, including: obtaining and calculating the pulse arrival time difference.

3. The method for separating ultra-high frequency partial discharge from multiple sources according to claim 2, characterized in that: Calculate the pulse arrival time difference, including: Get each pulse signal; For the same pulse signal, locate the wave head position received by two sensors; Based on the wave head position, obtaining a first receiving time and a second receiving time; The difference between the first receiving time and the second receiving time is calculated to obtain the pulse arrival time difference.

4. The method for separating ultra-high frequency partial discharge from multiple sources according to claim 3, characterized in that: The method of respectively calculating the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors further includes: obtaining a signal-to-noise ratio of the pulse signal; The signal-to-noise ratio is defined as the ratio of the amplitude of the peak value P to the minimum value N. For the same pulse signal, the pulse received by the two sensors with a larger signal-to-noise ratio is taken as the signal-to-noise ratio feature.

5. The method for separating ultra-high frequency partial discharge from multiple sources according to claim 4, characterized in that: The method of constructing a characteristic spectrum based on the calculated ultra-high frequency partial discharge pulse signal characteristics comprises: Obtain pulse arrival time difference and signal-to-noise ratio characteristics; A two-dimensional feature map is constructed with the pulse arrival time difference as the horizontal axis and the signal-to-noise ratio feature as the vertical axis.

6. The method for separating ultra-high frequency partial discharge from multiple sources according to claim 5, characterized in that: The step of judging the signal-to-noise ratio information in the characteristic spectrum and separating the noise signal from the effective partial discharge signal includes: Setting a first signal-to-noise ratio threshold; comparing the signal-to-noise ratio of each pulse signal with a first threshold; If it is lower than the first threshold, it is determined to be a noise signal and filtered; If it is greater than or equal to the first threshold, it is determined to be a valid partial discharge signal and is retained.

7. The method for separating ultra-high frequency partial discharge from multiple sources according to claim 6, characterized in that: The determining of the discharge type comprises: Obtain effective partial discharge signals and calculate the corresponding PRPD spectrum; By comparing the PRPD spectrum with the known defect spectrum, the type of partial discharge defect can be determined.

8. A system for the ultra-high frequency partial discharge multi-source separation method according to any one of claims 1 to 7, characterized in that: include: An acquisition module, used for acquiring ultra-high frequency partial discharge pulse signals through dual sensors; An acquisition module, used for respectively calculating the characteristics of the ultra-high frequency partial discharge pulse signals received by the two sensors; A feature map construction module, used to construct a feature map based on the calculated ultra-high frequency partial discharge pulse signal characteristics; The separation and judgment module is used to judge the signal-to-noise ratio information in the characteristic spectrum, separate the noise signal and the effective partial discharge signal, so as to determine the discharge type.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the ultra-high frequency partial discharge multi-source separation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the ultra-high frequency partial discharge multi-source separation method according to any one of claims 1 to 7.

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