A partial discharge detection method, device, electronic device and storage medium

Abstract

The present application discloses a partial discharge detection method, device, electronic device, and storage medium. The method and device are applied to an electronic device, specifically for collecting acoustic signals generated by partial discharge of a high-voltage electrical device to be detected to obtain acoustic data; based on the acoustic data, high-frequency acoustic signals are extracted through high-pass filtering, and the power spectral density of the energy envelope of the high-frequency acoustic signals is calculated; based on the energy distribution of the power spectral density of the energy envelope at a specific frequency, the discharge type of the high-voltage electrical device is identified. This solution is robust to external noise interference and effectively overcomes the problem of noise sensitivity existing in traditional partial discharge detection methods based on acoustic emission technology (such as PRPD pattern analysis), thereby significantly improving the detection ability for weak discharge signals.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment, and more specifically, to a partial discharge detection method, device, electronic device, and storage medium. Background Art

[0002] PD (Partial Discharges) is a common fault feature in high-voltage electrical equipment, often occurring at tiny defects or voids in electrical insulation materials. The long-term existence of PD will cause the gradual deterioration of the insulation material, which may ultimately lead to major failures or even equipment damage. Therefore, by using PD information to identify the insulation weak points of high-voltage electrical equipment, maintenance personnel can take timely maintenance measures to avoid unexpected system failures.

[0003] The existing PD detection methods for high-voltage electrical equipment are mainly based on acoustic emission. During the discharge process, due to the impact of molecules, tiny fractures in the insulation material, the generation and bursting of bubbles in the oil occur, and at the same time, the corresponding instantaneous stress wave, that is, the acoustic emission phenomenon, is generated. This phenomenon can be obtained by acoustic emission sensors deployed on the transformer shell. This method has the advantages of high sensitivity, high resistance to electrical interference, and accurate positioning.

[0004] For the discharge signals collected by partial discharge detection instruments in high-voltage electrical equipment, these signals contain key information such as the amplitude, phase, and time of the discharge. Currently, the most commonly used analysis method is the PRPD (Phase Resolved Partial Discharge) pattern. In the PRPD pattern, the amplitude of the discharge event is usually represented by the vertical coordinate (Y-axis), and the phase angle of the discharge event is represented by the horizontal coordinate (X-axis). By plotting dot diagrams in the coordinate system, the discharge activities during the entire AC cycle can be intuitively displayed, which can help us identify and analyze different types of partial discharge defects. However, in actual measurements, noise is inevitable. Given the sensitivity of the PRPD pattern to noise, it may be difficult to detect and analyze discharge events when the discharge signal is weak. Summary of the Invention

[0005] In view of this, the present application provides a partial discharge detection method, device, electronic device, and storage medium, which are used to solve the problem that it is difficult to detect weak discharge signals due to its sensitivity to noise in the PRPD pattern analysis.

[0006] To achieve the above object, the following solutions are proposed:

[0007] A partial discharge detection method is applied to an electronic device. The partial discharge detection method includes the steps:

[0008] Collect the acoustic signals generated by partial discharge of the high-voltage electrical equipment to be detected to obtain acoustic data;

[0009] Based on the acoustic data, extract high-frequency acoustic signals through high-pass filtering, and calculate the power spectral density of the energy envelope of the high-frequency acoustic signals;

[0010] Identify the discharge type of the high-voltage electrical equipment based on the energy distribution of the power spectral density at a specific frequency.

[0011] Optionally, the collecting the acoustic signals generated by partial discharge of the high-voltage electrical equipment to be detected to obtain acoustic data includes the steps of:

[0012] Perform acoustic sampling on the acoustic signals based on a fixed sampling frequency to obtain the acoustic data.

[0013] Optionally, the based on the acoustic data, extracting high-frequency acoustic signals through high-pass filtering, and calculating the power spectral density of the energy envelope of the high-frequency acoustic signals includes the steps of:

[0014] Perform high-pass filtering on the acoustic data to obtain high-frequency acoustic signals.

[0015] Calculate the energy envelope of the high-frequency acoustic signals;

[0016] Perform power spectral density estimation on the energy envelope to obtain the power spectral density.

[0017] Optionally, the performing high-pass filtering on the acoustic data to obtain high-frequency acoustic signals includes the steps of:

[0018] Perform Fourier transform on the acoustic data;

[0019] Perform high-pass filtering on the acoustical data after Fourier transform;

[0020] Perform inverse Fourier transform on the acoustical data after high-pass filtering to obtain the high-frequency acoustic signals.

[0021] Optionally, the performing power spectral density estimation on the energy envelope to obtain the power spectral density includes the steps of:

[0022] Calculate based on the following formula to obtain the power spectral density :

[0023]

[0024] where, is the power spectral density estimation operator, is the energy envelope, is the frequency of the energy envelope.

[0025] Optionally, the specific frequency includes the power grid fundamental frequency and the multiple frequencies of the power grid fundamental frequency.

[0026] Optionally, identifying the discharge type of the high-voltage electrical equipment based on the energy distribution of the power spectral density at a specific frequency includes the steps of:

[0027] When there is no peak or maximum value of the power spectral density at the power grid fundamental frequency, and there is no peak or maximum value of the power spectral density at the multiple frequencies of the power grid fundamental frequency, it is determined that the high-voltage electrical equipment is in a normal working state;

[0028] When the power spectral density has a peak or maximum value only at the multiple frequencies of the power grid fundamental frequency, it is determined that the high-voltage electrical equipment has a floating discharge or a surface discharge;

[0029] When the power spectral density has a peak or maximum value at the power grid fundamental frequency and has a peak or maximum value at the multiple frequencies of the power grid fundamental frequency, it is determined that the high-voltage electrical equipment has a corona discharge.

[0030] A partial discharge detection device is applied to an electronic device. The partial discharge detection device includes:

[0031] A signal acquisition module, configured to collect the acoustic signals generated by the partial discharge of the high-voltage electrical equipment to be detected, and obtain acoustic data;

[0032] A signal processing module, configured to extract high-frequency acoustic signals through high-pass filtering based on the acoustic data, and calculate the power spectral density of the energy envelope of the high-frequency acoustic signals;

[0033] A discharge identification module, configured to identify the discharge type of the high-voltage electrical equipment based on the energy distribution of the power spectral density at a specific frequency.

[0034] An electronic device, the electronic device includes at least one processor and a memory connected to the processor, wherein:

[0035] The memory is used to store computer programs or instructions;

[0036] The processor is used to execute the computer programs or instructions, so that the electronic device implements the partial discharge detection method as described above.

[0037] A computer-readable storage medium is applied to an electronic device. The storage medium carries one or more computer programs that can be executed by the electronic device, enabling the electronic device to implement the partial discharge detection method described above.

[0038] As can be seen from the above technical solutions, the present application discloses a partial discharge detection method, device, electronic device, and storage medium. The method and device are applied to an electronic device, specifically for collecting acoustic signals generated by partial discharges of high-voltage electrical equipment to be detected to obtain acoustic data; based on the acoustic data, high-frequency acoustic signals are extracted through high-pass filtering, and the power spectral density of the energy envelope of the high-frequency acoustic signals is calculated; based on the energy distribution of the power spectral density of the energy envelope at specific frequencies, the discharge type of the high-voltage electrical equipment is identified. This solution is robust to external noise interference, thereby being able to solve the problem that traditional partial discharge detection methods based on acoustic emission (such as PRPD pattern analysis) are sensitive to noise and difficult to detect weak discharge signals. Description of the Drawings

[0039] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0040] Figure 1 It is a flowchart of a partial discharge detection method according to an embodiment of the present application;

[0041] Figure 2a It is the PRPD pattern and the power density spectrum of the energy envelope of the high-frequency acoustic signal of the high-voltage electrical equipment in normal operation according to an embodiment of the present application;

[0042] Figure 2b It is the PRPD pattern and the power density spectrum of the energy envelope of the high-frequency acoustic signal of the high-voltage electrical equipment in floating discharge according to an embodiment of the present application;

[0043] Figure 2c It is the PRPD pattern and the power density spectrum of the energy envelope of the high-frequency acoustic signal of the high-voltage electrical equipment in surface discharge according to an embodiment of the present application;

[0044] Figure 2d It is the PRPD pattern and the power density spectrum of the energy envelope of the high-frequency acoustic signal of the high-voltage electrical equipment in corona discharge according to an embodiment of the present application;

[0045] Figure 2eThis is the PRPD pattern of high-voltage electrical equipment during weak discharge and the power density spectrum of the energy envelope of high-frequency acoustic signals in the embodiments of the present application;

[0046] Figure 2f This is the PRPD pattern of high-voltage electrical equipment during weak discharge and the power density spectrum of the energy envelope of high-frequency acoustic signals in the embodiments of the present application;

[0047] Figure 3 This is the block diagram of a partial discharge detection device in the embodiments of the present application;

[0048] Figure 4 This is the block diagram of an electronic device in the embodiments of the present application. Specific embodiments

[0049] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0050] Figure 1 This is the flowchart of a partial discharge detection method in the embodiments of the present application.

[0051] As Figure 1 shown, the partial discharge detection method provided in this embodiment is applied to an electronic device for detecting the partial discharge of high-voltage electrical equipment. The electronic device can be understood as a computer, server, or cloud platform with information processing capabilities and data calculation capabilities. The partial discharge detection method includes the following steps:

[0052] S1. Collect the acoustic signals generated by the partial discharge of the high-voltage electrical equipment to be detected to obtain acoustic data.

[0053] During the sampling process, use an acoustic sensor to sample the acoustic signals generated by the partial discharge of the high-voltage electrical equipment at a fixed sampling frequency The sampled signal is a discrete-time sequence x[n], where n is the index of the sampling point. The sampling process is expressed as follows:

[0054] x[n]=s( t n ) (1)

[0055] Among them, t) is the continuous acoustic signal, , is the time corresponding to the nth sampling point, and the sampling frequency kHz.

[0056] S2. Based on the acoustic data, extract the high-frequency acoustic signal through high-pass filtering, and calculate the power spectral density of the energy envelope of the high-frequency acoustic signal.

[0057] Specifically, perform high-pass filtering, energy envelope calculation, and power spectral density estimation on the acoustic data to obtain the power spectral density. The specific processing process is as follows:

[0058] S201. Perform high-pass filtering on the acoustic data to obtain the high-frequency acoustic signal.

[0059] According to the characteristics of the radiation acoustic signal of high-voltage electrical equipment (mainly concentrated within 2000 Hz, and the signal above 2000 Hz attenuates by more than 30 dB) and the characteristics of the partial discharge radiation acoustic signal (wide frequency band, up to dozens of kHz), perform high-pass filtering on the above acoustic data, and the filtering frequency band range is above 16 kHz. The specific process is as follows:

[0060] First, perform fast Fourier transform processing on the acoustic data, and the obtained data is expressed as X(f),

[0061] X(f)=FFT( x[n]) (2)

[0062] where FFT(∙) represents the fast Fourier transform.

[0063] Then, perform high-pass filtering on X(f), and the changed expression is as follows:

[0064] X(f) (3)

[0065] Finally, perform inverse fast Fourier transform to obtain the high-frequency acoustic signal y[n]:

[0066] y[n] (4)

[0067] where IFFT(∙) represents the inverse fast Fourier transform.

[0068] S202. Calculate the energy envelope of the high-frequency acoustic signal.

[0069] The energy envelope, also known as the signal energy envelope or instantaneous power envelope, is an important concept in signal processing. It describes the energy distribution characteristics of the signal in the time domain or frequency domain, and can intuitively display the energy intensity of the signal at different time or frequency points, which is helpful for analyzing the dynamic characteristics and change trends of the signal. For the partial discharge signal, its energy envelope shows periodicity related to the power frequency cycle. y[n] is the filtered signal, and the energy envelope can be obtained through simple moving average filter calculation :

[0070] E n = 1 N ∑ k=0 N - 1 y[ N - k] 2 (5)

[0071] Among them, N is the window size, and k is the sequence number of the signal within the window.

[0072] S203. Perform power spectral density estimation on the energy envelope to obtain the power spectral density.

[0073] Since the energy envelope of the partial discharge signal is related to the power frequency period, the energy distribution of its power spectral density at specific frequencies related to the power grid fundamental frequency (such as 50 Hz or 100 Hz) will be relatively concentrated. This means that at these frequencies, the signal contains more energy or power.

[0074] (6)

[0075] Among them, represents the power spectral density, represents the power spectral density estimation operator. During the calculation process, the energy below 20 Hz of is set to 0 to eliminate interference.

[0076] S3. Identify the discharge type based on the power spectral density of the energy envelope.

[0077] That is, identify based on the energy distribution of the power spectral density at specific frequencies obtained above to determine the discharge type of the high-voltage electrical equipment. Since the power spectral density is strongly correlated with the power grid fundamental frequency, the specific frequencies here are selected as the power grid fundamental frequency and its harmonic frequencies. Given that most countries choose 50 Hz as the power grid frequency, the power grid fundamental frequency here is taken as an example of 50 Hz, and its second harmonic is 100 Hz. The following describes the specific identification process of the discharge type using the 50 Hz power grid fundamental frequency:

[0078] When a specific type of discharge event occurs in the high-voltage electrical equipment, the energy distribution of the power spectral density at the above specific frequencies, such as at 50 Hz and 100 Hz, occupies a more prominent position.

[0079] In the analysis of the voiceprint sample library, when the high-voltage electrical equipment is in the normal working state, such as Figure 2a as shown, there are no peaks or maximum values in the power spectral density of the acoustic wave signal of the high-voltage electrical equipment at 50 Hz and 100 Hz. At this time, it can be determined that the high-voltage electrical equipment such as the transformer is in the normal working state and no partial discharge has occurred.

[0080] Specifically, when suspension discharge or surface discharge occurs in the high-voltage electrical equipment, such asFigure 2b and Figure 2c As shown in Figure 2c , there will be two discharge events within one power frequency cycle. This discharge pattern causes the energy distribution at only 100 Hz to be significantly enhanced, becoming the main peak or maximum value. That is, it can be determined that the high-voltage electrical equipment has floating discharge or surface discharge based on the peak or maximum value of the power spectral density corresponding to the frequency of 100 Hz.

[0081] On the other hand, when corona discharge occurs in the high-voltage electrical equipment, as Figure 2d shown, the discharge event occurs only once within one power frequency cycle. This discharge pattern causes the energy at 50 Hz to dominate, becoming the peak or maximum value, and there is a peak or maximum value for the energy at 100 Hz. At this time, it can be determined that the high-voltage electrical equipment has corona discharge based on the peak or maximum value of the power spectral density corresponding to the frequency of 50 Hz and the existence of a peak or maximum value for the power spectral density corresponding to the frequency of 100 Hz.

[0082] In addition, when weak discharge or faint discharge occurs, it is difficult to identify the discharge characteristics from the PRPD pattern. However, the local discharge signal energy envelope analysis based on acoustic fingerprint detection in this application can still accurately identify the discharge and can determine the number of discharge events within one power frequency cycle. As Figure 2e and Figure 2f shown, when weak discharge occurs, there is an obvious peak in the power spectral density at 100 Hz, and this peak is also the maximum value at the same time; when faint discharge occurs, there is a peak in the power spectral density at 50 Hz and 100 Hz respectively, and the peak that appears at 50 Hz is also the maximum value at the same time. Based on the above characteristics of the power spectral density, weak discharge and faint discharge can be identified.

[0083] It can be seen from the above technical solutions that this embodiment provides a local discharge detection method, which is applied to an electronic device. Specifically, it collects the acoustic signals generated by the local discharge of the high-voltage electrical equipment to be detected to obtain acoustic data; based on the acoustic data, it extracts high-frequency acoustic signals through high-pass filtering and calculates the power spectral density of the energy envelope of the high-frequency acoustic signals; it identifies the discharge type of the high-voltage electrical equipment based on the energy distribution of the power spectral density of the energy envelope at specific frequencies. This solution is robust to external noise interference, thereby being able to solve the problem that traditional local discharge detection methods based on acoustic emission (such as PRPD pattern analysis) are sensitive to noise and difficult to detect weak discharge signals.

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0085] Although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in a sequential order. In certain circumstances, multitasking and parallel processing may be advantageous.

[0086] It should be understood that the various steps recited in the method embodiments of the present disclosure may be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit steps shown. The scope of the present disclosure is not limited in this regard.

[0087] Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages or combinations thereof, including but not limited to object-oriented programming languages such as Java, Smalltalk, C++, and also including conventional procedural programming languages such as the C language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0088] Figure 3 It is a block diagram of a partial discharge detection device according to an embodiment of the present application.

[0089] As Figure 3As shown, the partial discharge detection device provided in this embodiment is applied to an electronic device for detecting partial discharge of high-voltage electrical equipment. The electronic device can be understood as a computer, server or cloud platform with information processing and data calculation capabilities. The partial discharge detection device includes a signal acquisition module 10, a signal processing module 20, and a discharge identification module 30.

[0090] The signal acquisition module is used to collect the acoustic signals generated by the partial discharge of the high-voltage electrical equipment to be detected, and obtain acoustic data.

[0091] During the sampling process, an acoustic sensor is used to sample the acoustic signals generated by the partial discharge of the high-voltage electrical equipment at a fixed sampling frequency The sampled signal is a discrete-time sequence x[n], where n is the index of the sampling point. The sampling process is expressed as follows:

[0092] x[n]=s( t n ) (1)

[0093] where t) is the continuous acoustic signal, , is the time corresponding to the nth sampling point, and the sampling frequency kHz.

[0094] The signal processing module is used to extract high-frequency acoustic signals based on the acoustic data through high-pass filtering, and calculate the power spectral density of the energy envelope of the high-frequency acoustic signals. Specifically, high-pass filtering, energy envelope calculation, and power spectral density estimation are performed on the acoustic data to obtain the power spectral density. The module includes a filtering processing module, an envelope calculation module, and a power spectral density estimation module.

[0095] The filtering processing module is used to perform high-pass filtering on the acoustic data to obtain high-frequency acoustic signals.

[0096] According to the characteristics of the radiation acoustic signals of high-voltage electrical equipment (mainly concentrated within 2000 Hz, and the signals above 2000 Hz attenuate by more than 30 dB) and the characteristics of the radiation acoustic signals of partial discharge (wide frequency band, up to dozens of kHz), the above acoustic data is subjected to high-pass filtering, and the filtering frequency band range is above 16 kHz. The module specifically includes a first transformation unit, a filtering execution unit, and a second transformation unit.

[0097] The first transformation unit is used to perform fast Fourier transform on the acoustic data to obtain the data represented as X(f),

[0098] X(f)=FFT( x[n]) (2)

[0099] Among them, FFT(∙) represents the fast Fourier transform.

[0100] The filtering execution unit is used to perform high-pass filtering on X(f), and the changed expression is as follows:

[0101] X(f) (3)

[0102] The second transformation unit is used to perform the inverse fast Fourier transform to obtain the high-frequency acoustic signal y[n]:

[0103] y[n] (4)

[0104] Among them, IFFT(∙) represents the inverse fast Fourier transform.

[0105] The envelope calculation module is used to calculate the energy envelope of the high-frequency acoustic signal.

[0106] The energy envelope, also known as the signal energy envelope or instantaneous power envelope, is an important concept in signal processing. It describes the energy distribution characteristics of a signal in the time domain or frequency domain, and can intuitively display the energy intensity of the signal at different time or frequency points, which helps to analyze the dynamic characteristics and change trends of the signal. For partial discharge signals, their energy envelopes show periodicity related to the power frequency cycle. y[n] is the filtered signal, and this energy envelope can be obtained through simple moving average filter calculation :

[0107] E n = 1 N ∑ k=0 N - 1 y[ N - k] 2 (5)

[0108] Among them, N is the window size, and k is the sequence number of the signal within the window.

[0109] The power spectral density estimation module is used to estimate the power spectral density of the energy envelope to obtain the power spectral density.

[0110] Since the energy envelope of the partial discharge signal is related to the power frequency cycle, the energy distribution of its power spectral density is relatively concentrated at specific frequencies (such as 50Hz or 100Hz). This means that at these frequencies, the signal contains more energy or power.

[0111] (6)

[0112] Among them, represents the power spectral density, represents the power spectral density estimation operator. During the calculation, The energy below 20 Hz is set to 0 to eliminate interference.

[0113] The discharge identification module is used to identify the discharge type based on the power spectral density of the energy envelope.

[0114] That is, it is identified based on the energy distribution of the power spectral density at specific frequencies obtained above to determine the discharge type of the high-voltage electrical equipment. Since the power spectral density is strongly correlated with the power grid fundamental frequency, the specific frequencies here are the power grid fundamental frequency and its multiples. Given that most countries choose 50 Hz as the power grid frequency, the power grid fundamental frequency here is taken as an example of 50 Hz, and its first multiple is 100 Hz. The following illustrates the specific identification process of the discharge type with a power grid fundamental frequency of 50 Hz:

[0115] In the analysis of the voiceprint sample library, when the high-voltage electrical equipment is in a normal operating state, as shown in Figure 2(a), the energy occupied by the ambient noise at 50 Hz or 100 Hz frequencies does not have a peak or maximum value. At this time, the first identification unit can determine that the high-voltage electrical equipment such as a transformer is in a normal operating state and determine that no partial discharge has occurred.

[0116] When a specific type of discharge event occurs in the high-voltage electrical equipment, the energy distribution of the power spectral density at the above-mentioned specific frequencies, such as at 50 Hz and 100 Hz, occupies a more prominent position.

[0117] In the analysis of the voiceprint sample library, when the high-voltage electrical equipment is in a normal operating state, as shown in Figure 2(a), the power spectral density of the acoustic wave signal of the high-voltage electrical equipment does not have a peak or a maximum value at 50 Hz and 100 Hz. At this time, it can be determined that the high-voltage electrical equipment such as a transformer is in a normal operating state and determine that no partial discharge has occurred.

[0118] Specifically, when a floating discharge or a surface discharge occurs in the high-voltage electrical equipment, such as Figure 2b and Figure 2c shown, there will be two discharge events within one power frequency cycle. This discharge mode causes the energy distribution at only 100 Hz to be significantly enhanced and become the main peak or maximum value. The second identification unit can then determine that a floating discharge or a surface discharge has occurred in the high-voltage electrical equipment based on the power spectral density corresponding to 100 Hz being a peak or a maximum value.

[0119] On the other hand, when a corona discharge occurs in the high-voltage electrical equipment, such as Figure 2dAs shown, the discharge event occurs only once within one power frequency cycle. This discharge pattern causes the energy at 50 Hz to dominate and become the peak or maximum value, and there is also a peak or maximum value for the energy at 100 Hz. At this time, the third identification unit can determine that the high-voltage electrical equipment has corona discharge based on the fact that the power spectral density corresponding to 50 Hz is the peak or maximum value, and the power spectral density corresponding to 100 Hz has a peak or maximum value.

[0120] In addition, when weak discharge or faint discharge occurs, it is difficult to identify the discharge characteristics from the PRPD pattern. However, the local discharge signal energy envelope analysis based on acoustic fingerprint detection in this application can still accurately identify the discharge and can determine the number of discharge events occurring within one power frequency cycle. As Figure 2e and Figure 2f shown, when weak discharge occurs, there is an obvious peak in the power spectral density at 100 Hz, and this peak is also the maximum value at the same time; when faint discharge occurs, there is a peak in the power spectral density at 50 Hz and 100 Hz respectively, and the peak appearing at 50 Hz is also the maximum value at the same time. Based on the above characteristics of the power spectral density, weak discharge and faint discharge can be identified.

[0121] As can be seen from the above technical solution, this embodiment provides a partial discharge detection device, which is applied to an electronic device. Specifically, it collects acoustic signals generated by partial discharge of a high-voltage electrical equipment to be detected to obtain acoustic data; based on the acoustic data, high-frequency acoustic signals are extracted through high-pass filtering, and the power spectral density of the energy envelope of the high-frequency acoustic signals is calculated; the discharge type of the high-voltage electrical equipment is identified based on the energy distribution of the power spectral density of the energy envelope at specific frequencies. This solution is robust to external noise interference, thus being able to solve the problem that traditional partial discharge detection methods based on acoustic emission (such as PRPD pattern analysis) are sensitive to noise and difficult to detect weak discharge signals.

[0122] The units involved in the embodiments described in this disclosure can be implemented in software or in hardware. Among them, the name of the unit does not constitute a limitation to the unit itself in some cases. For example, the first acquisition unit can also be described as "the unit for acquiring at least two Internet protocol addresses".

[0123] The functions described above herein can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and so on.

[0124] Figure 4 A block diagram of an electronic device according to an embodiment of the present application.

[0125] Reference is made below to Figure 4 , which shows a schematic structural diagram of an electronic device suitable for implementing the electronic device in the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. This electronic device is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0126] The electronic device may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 401, which may perform various appropriate actions and processes according to a program stored in the read-only memory ROM 402 or a program loaded from the input device 406 into the random access memory RAM 403. In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, the ROM, and the RAM are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0127] Generally, the following devices may be connected to the I / O interface: input devices including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an electronic device having various devices, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be alternatively implemented or had.

[0128] This embodiment also provides an embodiment of a computer-readable storage medium.

[0129] The above computer-readable storage medium is applied to an electronic device and carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device collects acoustic signals generated by partial discharge of a high-voltage electrical device to be detected, obtaining acoustic data; based on the acoustic data, high-frequency acoustic signals are extracted through high-pass filtering, and the power spectral density of the energy envelope of the high-frequency acoustic signals is calculated; based on the energy distribution of the power spectral density of the energy envelope at specific frequencies, the discharge type of the high-voltage electrical device is identified. This solution is robust to external noise interference, thereby being able to solve the problem that traditional partial discharge detection methods based on acoustic emission (such as PRPD pattern analysis) are sensitive to noise and difficult to detect weak discharge signals.

[0130] It should be noted that the computer-readable medium in the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0131] In the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. And in the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0132] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0133] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

[0134] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the said element.

[0135] The technical solutions provided by the present invention have been introduced in detail above. Specific examples are used herein to illustrate the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A partial discharge detection method, applied to an electronic device, characterized in that, The described partial discharge detection method includes the steps of: Collecting acoustic signals generated by partial discharge of a high-voltage electrical device to be detected based on a fixed sampling frequency to obtain acoustic data; Performing high-pass filtering on the acoustic data to obtain a high-frequency acoustic signal y[n], where n is the index of the sampling point; Calculating the energy envelope of the high-frequency acoustic signal through a moving average filter based on the following formula: where N is the window size and k is the sequence number of the signal within the window; The energy envelope is used to display the energy intensity of the high-frequency acoustic signal at different time or frequency points and presents periodicity related to the power frequency period; Calculating based on the following formula to obtain the power spectral density P(f): Among them, PSD(·) is the power spectral density estimation operator, E n is the energy envelope, and f is the frequency of the energy envelope; Identifying the discharge type of the high-voltage electrical device based on the energy distribution of the power spectral density at specific frequencies, where the specific frequencies include the power grid fundamental frequency and the multiples of the power grid fundamental frequency.

2. The partial discharge detection method according to claim 1, characterized in that, The step of performing high-pass filtering on the acoustic data to obtain a high-frequency acoustic signal y[n] includes the steps of: Performing Fourier transform processing on the acoustic data; Performing high-pass filtering on the acoustical data after Fourier transform; Performing inverse Fourier transform processing on the acoustical data after high-pass filtering to obtain the high-frequency acoustic signal.

3. The partial discharge detection method according to claim 1, wherein The step of identifying the discharge type of the high-voltage electrical device based on the energy distribution of the power spectral density at specific frequencies includes the steps of: When there is no peak or maximum value at the power grid fundamental frequency of the power spectral density and there is no peak or maximum value at the multiples of the power grid fundamental frequency of the power spectral density, it is determined that the high-voltage electrical device is in a normal operating state; When the power spectral density has a peak or maximum value only at the multiples of the power grid fundamental frequency, it is determined that the high-voltage electrical device has a floating discharge or a surface discharge; When the power spectral density has a peak or maximum value at the power grid fundamental frequency and has a peak or maximum value at the multiples of the power grid fundamental frequency, it is determined that the high-voltage electrical device has a corona discharge.

4. A partial discharge detection device is applied to an electronic device, characterized in that The described partial discharge detection device includes: A signal acquisition module configured to collect acoustic signals generated by partial discharge of a high-voltage electrical device to be detected based on a fixed sampling frequency to obtain acoustic data; A signal processing module configured to perform high-pass filtering on the acoustic data to obtain a high-frequency acoustic signal y[n], where n is the index of the sampling point; calculating the energy envelope of the high-frequency acoustic signal through a moving average filter based on the following formula: where N is the window size and k is the sequence number of the signal within the window; The energy envelope is used to display the energy intensity of the high-frequency acoustic signal at different time or frequency points and presents periodicity related to the power frequency period; calculating based on the following formula to obtain the power spectral density P(f): where PSD(·) is the power spectral density estimation operator, E n is the energy envelope, and f is the frequency of the energy envelope; A discharge identification module configured to identify the discharge type of the high-voltage electrical device based on the energy distribution of the power spectral density at specific frequencies, where the specific frequencies include the power grid fundamental frequency and the multiples of the power grid fundamental frequency.

5. An electronic device, characterized in that, The electronic device includes at least one processor and a memory connected to the processor, where: The memory is used to store computer programs or instructions; The processor is used to execute the computer programs or instructions, so that the electronic device implements the partial discharge detection method described in any one of claims 1 to 3.

6. A computer-readable storage medium, applied to an electronic device, characterized in that, The storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device can implement the partial discharge detection method described in any one of claims 1 to 3.

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