Transformer partial discharge detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a transformer partial discharge detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring pulse current of a transformer; wherein the pulse current comprises a pulse current of a transformer grounding wire and a pulse current of a transformer winding; extracting a plurality of characteristic parameters according to the pulse current of the transformer; according to the plurality of characteristic parameters, judging whether the transformer has a partial discharge phenomenon or not; acquiring a plurality of ultrasonic signals of the transformer under the condition that the partial discharge phenomenon of the transformer is determined; performing filtering processing, noise reduction processing and amplification processing on each ultrasonic signal to obtain a processed ultrasonic signal; establishing a positioning equation based on the receiving time difference of different ultrasonic signals according to the processed ultrasonic signals; and solving the positioning equation, and determining the position of the partial discharge source of the transformer. According to the invention, the detection precision of the partial discharge of the transformer can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a transformer partial discharge detection method, device, electronic equipment and storage medium. Background Art

[0002] Transformers are key equipment in power systems, and their operating conditions are directly related to the safety and stability of the entire power system. Partial discharge is one of the important signals of insulation aging or defects inside transformers, which can lead to a gradual decline in insulation performance and may eventually cause equipment failure or damage. Accurate and timely detection of partial discharge in transformers is of great significance for preventing transformer failures and extending their service life.

[0003] At present, the main detection method for transformer partial discharge is the pulse current method. The pulse current method can more sensitively identify whether there is a partial discharge phenomenon by monitoring the current pulse generated by partial discharge at the grounding end. However, during the propagation of the current signal inside the transformer and the grounding system, it will be interfered and attenuated by various factors, resulting in signal diffusion, making it difficult to accurately locate the partial discharge source. Summary of the invention

[0004] The embodiments of the present invention provide a transformer partial discharge detection method, device, electronic device and storage medium. By implementing the present invention, the partial discharge source can be accurately located by using an ultrasonic method on the basis of detecting the partial discharge of the transformer by a pulse current method, thereby improving the accuracy of transformer partial discharge detection.

[0005] An embodiment of the present invention provides a transformer partial discharge detection method, comprising: obtaining a pulse current of the transformer; wherein the pulse current comprises a pulse current of a transformer grounding wire and a pulse current of a transformer winding.

[0006] According to the pulse current of the transformer, several characteristic parameters are extracted.

[0007] According to the characteristic parameters, it is determined whether the transformer has partial discharge.

[0008] When it is determined that the transformer has partial discharge, several ultrasonic signals of the transformer are obtained.

[0009] Each ultrasonic signal is subjected to filtering, noise reduction and amplification processing to obtain a processed ultrasonic signal.

[0010] According to the processed ultrasonic signal, a positioning equation is established based on the reception time difference of different ultrasonic signals; and the positioning equation is solved to determine the position of the local discharge source of the transformer.

[0011] Furthermore, according to the pulse current of the transformer, several characteristic parameters are extracted, including:

[0012] The pulse current is filtered to generate a filtered signal.

[0013] The filtered signal is subjected to noise reduction processing to generate a noise reduction signal.

[0014] The noise reduction signal is amplified to generate an amplified pulse signal.

[0015] The local discharge signal feature quantity is extracted from the amplified pulse signal to obtain several feature parameters.

[0016] Furthermore, the performing noise reduction processing on the filtered signal to generate a noise reduction signal includes:

[0017] An autocorrelation function of the filtered signal is calculated.

[0018] The autocorrelation function is analyzed to identify periodic signals and random noise.

[0019] The random noise is denoised according to a preset global threshold to generate a denoised signal.

[0020] Furthermore, the amplified pulse signal is subjected to partial discharge signal feature extraction to obtain several feature parameters, including:

[0021] The amplified pulse signal is subjected to empirical mode decomposition processing to generate a series of intrinsic mode function components.

[0022] Each intrinsic mode function component is subjected to singular value decomposition to obtain several characteristic parameters.

[0023] Further, solving the positioning equation to determine the location of the local discharge source of the transformer includes:

[0024] The least square method is used to solve the positioning equation to generate a first positioning result.

[0025] The positioning result is iteratively and robustly estimated using the robust weighted total least squares method until the positioning result meets the preset accuracy requirement, thereby generating a second positioning result.

[0026] The second positioning result is used as the location of the partial discharge source of the transformer.

[0027] Furthermore, after solving the positioning equation and determining the location of the partial discharge source of the transformer, the method further includes:

[0028] According to the characteristic parameters and the location of the partial discharge source, the type of partial discharge is identified and the severity of the partial discharge is evaluated.

[0029] Perform trend analysis based on the type of partial discharge and the severity of partial discharge to assess the development trend of the transformer insulation condition.

[0030] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0031] An embodiment of the present invention provides a transformer partial discharge detection device, comprising: a pulse current acquisition module, a characteristic parameter extraction module, a partial discharge judgment module, an ultrasonic signal acquisition module, a signal processing module and a partial discharge source positioning module;

[0032] The pulse current acquisition module is used to acquire the pulse current of the transformer, wherein the pulse current includes the pulse current of the transformer grounding wire and the pulse current of the transformer winding.

[0033] The characteristic parameter extraction module is used to extract a number of characteristic parameters according to the pulse current of the transformer.

[0034] The partial discharge judgment module is used to judge whether there is a partial discharge phenomenon in the transformer according to the several characteristic parameters.

[0035] The ultrasonic signal acquisition module is used to acquire a number of ultrasonic signals of the transformer when it is determined that the transformer has a partial discharge phenomenon.

[0036] The signal processing module is used to perform filtering, noise reduction and amplification processing on each ultrasonic signal to obtain a processed ultrasonic signal.

[0037] The local discharge source positioning module is used to establish a positioning equation based on the processed ultrasonic signal and the reception time difference of different ultrasonic signals; solve the positioning equation to determine the position of the local discharge source of the transformer.

[0038] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0039] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the transformer partial discharge detection method described in any one of the above method embodiments can be implemented.

[0040] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0041] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the transformer partial discharge detection method described in any one of the above method embodiments can be implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The embodiment of the present invention provides a transformer partial discharge detection method, device, electronic device and storage medium. The method extracts features of pulse current and generates a plurality of characteristic parameters. By analyzing the plurality of characteristic parameters, it is determined whether the transformer has a partial discharge phenomenon. If the partial discharge phenomenon exists, a plurality of ultrasonic signals of the transformer are obtained, and signal processing is performed on the plurality of ultrasonic signals. The location of the partial discharge source of the transformer is solved according to the processed ultrasonic signals.

[0044] The present invention determines whether there is partial discharge in the transformer by detecting the pulse current of the transformer grounding wire and winding; and then locates the source of partial discharge by detecting ultrasonic signals. The ultrasonic method locates the discharge source by detecting the acoustic wave signal caused by partial discharge. Since the acoustic wave signal is not interfered by the complex electricity and magnetism inside the transformer, it has higher spatial positioning accuracy than the pulse current method. This solves the problem of inaccurate positioning of the pulse current method in the prior art and improves the detection accuracy of partial discharge of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of a transformer partial discharge detection method provided by one embodiment of the present invention.

[0046] Figure 2 It is a flow chart of a transformer partial discharge detection method provided by another embodiment of the present invention.

[0047] Figure 3 It is a structural schematic diagram of a transformer partial discharge detection device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, an embodiment of the present invention provides a transformer partial discharge detection method, which at least includes the following steps:

[0050] Step S1, obtaining the pulse current of the transformer;

[0051] Specifically, the pulse current includes the pulse current of the transformer grounding wire and the pulse current of the transformer winding. Partial discharge will induce a small pulse current in the grounding loop, and the current signal of the grounding wire can be used as a key indicator for detecting partial discharge activities. In addition, the partial discharge phenomenon will generate electromagnetic interference inside the transformer winding. By detecting the pulse current in the winding, relevant information about the partial discharge can be captured. The pulse current is detected by a current sensor, which is a Rogowski coil.

[0052] Step S2, extracting several characteristic parameters according to the pulse current of the transformer;

[0053] In an optional embodiment, the extracting of several characteristic parameters according to the pulse current of the transformer includes:

[0054] Performing filtering on the pulse current to generate a filtering signal;

[0055] Performing noise reduction processing on the filtered signal to generate a noise reduction signal;

[0056] amplifying the noise reduction signal to generate an amplified pulse signal;

[0057] The local discharge signal feature quantity is extracted from the amplified pulse signal to obtain several feature parameters.

[0058] In a specific implementation, filtering is performed through a digital filter to remove specific frequency components in the signal and retain useful signal components.

[0059] The denoising process is performed through the spatiotemporal denoising method of the autocorrelation function to eliminate random noise and interference in the signal and retain the effective characteristics of the original signal.

[0060] The signal is amplified by an amplifier to enhance its amplitude, making the signal characteristics more obvious and facilitating subsequent processing and analysis.

[0061] Optionally, performing noise reduction processing on the filtered signal to generate a noise reduction signal includes:

[0062] Step S2.1, calculate the autocorrelation function of the filtered signal. The autocorrelation function is used to measure the similarity of the signal under different time delays and help capture the periodic characteristics in the signal. By calculating the autocorrelation function, the correlation of the signal at different time intervals can be analyzed, thus laying the foundation for the subsequent steps.

[0063] Step S2.2, identify the periodic components, analyze the autocorrelation function, and identify the peaks corresponding to the signal period. By analyzing the results of the autocorrelation function, focus on the peaks in the autocorrelation function. The periodic components will appear as peaks that appear regularly in the autocorrelation function, and the intervals between these peaks usually correspond to the periodic characteristics of the signal. By identifying these peaks related to the period, the main periodic components in the partial discharge signal can be distinguished.

[0064] Step S2.3: Determine the demarcation point between noise and signal. Determine the demarcation point between random noise and periodic signal by observing the peak distribution of the autocorrelation function. In order to distinguish the random noise and effective signal components in the signal, it is necessary to further analyze the peak distribution of the autocorrelation function. Periodic signals will show regular peaks, while random noise will not form obvious periodicity. By observing the decay rate of the autocorrelation function and the interval between the peaks, the demarcation point between noise and signal can be determined, and it can be found which parts of the signal are dominated by noise and which parts contain useful signal information.

[0065] Step S2.4: De-noising, using a preset global threshold to denoise the random noise in the autocorrelation function. Once the demarcation point between noise and signal is determined, the random noise in the autocorrelation function is denoised using a preset global threshold. The selection of the global threshold is critical, as it determines which signal parts are retained and which noise parts are removed. By weakening or removing the parts of the autocorrelation function that are below the threshold, the impact of random noise can be significantly reduced, thereby retaining the main periodic components and features of the signal. Among them, the global threshold can be flexibly selected according to actual conditions.

[0066] Step S2.5: Reconstruct the denoised signal to generate a denoised signal. After the denoising process is completed, the retained signal features need to be reconstructed. The denoised autocorrelation function is reversely processed to generate a reconstructed denoised signal.

[0067] In a preferred embodiment, the amplified pulse signal is subjected to partial discharge signal feature extraction to obtain several feature parameters, including:

[0068] Performing empirical mode decomposition processing on the amplified pulse signal to generate a series of intrinsic mode function components;

[0069] Each intrinsic mode function component is subjected to singular value decomposition to obtain several characteristic parameters.

[0070] Specifically, the amplified pulse signal is processed by empirical mode decomposition (EMD). EMD is an adaptive signal decomposition method that can decompose the complex original signal into multiple intrinsic mode function (IMF) components with different frequencies. Each IMF component represents a component with different characteristic frequencies and time scales in the signal, and can capture the complex time-frequency characteristics hidden in the partial discharge signal. In this way, the partial discharge signal is decomposed into a series of IMF components reflecting different frequency components, which lays the foundation for subsequent feature extraction.

[0071] Next, singular value decomposition (SVD) is performed on each generated intrinsic mode function (IMF) component. SVD is a linear algebra method that can decompose the signal matrix into multiple singular values ​​and extract the main features of the signal through the distribution of the singular values. Specifically, by performing singular value decomposition on each IMF component, the main characteristic parameters in the signal are extracted. The size of the singular value can reflect the energy concentration of the signal and the influence of the main component. These characteristic parameters include the amplitude, frequency, time domain characteristics, etc. of the signal, which can fully reflect the time-frequency characteristics of partial discharge.

[0072] It should be noted here that the characteristic parameters include: discharge amount, detection sensitivity, measurement frequency band, gain dynamic range, filter parameters, partial discharge pulse waveform, partial discharge starting voltage, pulse frequency, pulse amplitude, high frequency current (HFCT) signal, and number of discharges.

[0073] Step S3, judging whether there is partial discharge in the transformer according to the characteristic parameters;

[0074] In actual operation, the characteristic parameters are compared with preset partial discharge reference standards to determine whether the transformer has partial discharge phenomenon. The preset partial discharge reference parameters are determined by statistical analysis of historical records and empirical data.

[0075] It should be noted that the pulse current includes the pulse current of the transformer grounding wire and the pulse current of the transformer winding. By processing the pulse current of the transformer grounding wire and the pulse current of the transformer winding respectively, two sets of characteristic parameters for judging whether the transformer has partial discharge phenomenon can be obtained. In an optional embodiment, as long as a set of characteristic parameters is compared with a preset partial discharge reference standard and it is considered that there is a partial discharge phenomenon, the transformer is considered to have a partial discharge phenomenon. The conclusions drawn from the two sets of characteristic parameters can be used for subsequent evaluation of the severity of partial discharge of the transformer.

[0076] Step S4: when it is determined that the transformer has a partial discharge phenomenon, obtaining a plurality of ultrasonic signals of the transformer;

[0077] It should be noted here that the several ultrasonic signals are obtained by ultrasonic sensors installed on the transformer casing. These sensors are capable of capturing ultrasonic signals generated by partial discharge phenomena. The selection of specific locations can be determined based on the structural characteristics of the transformer and the high-risk areas where partial discharge occurs, to ensure that the ultrasonic signals generated by the discharge can be efficiently captured. In order to accurately locate the location of the local discharge source by synchronously collecting the time difference of the signals at multiple points, it is generally necessary to install multiple ultrasonic sensors on the transformer casing. This multi-sensor configuration significantly improves the accuracy and anti-interference ability of local discharge positioning, and is the key to achieving accurate positioning of transformer partial discharge. Generally speaking, the number of ultrasonic sensors is at least 3.

[0078] Step S5, performing filtering, noise reduction and amplification processing on each ultrasonic signal to obtain a processed ultrasonic signal;

[0079] In an optional embodiment, each ultrasonic signal is filtered by a digital filter to remove specific frequency components in the signal and retain useful signal components.

[0080] Each ultrasonic signal is subjected to denoising by a spatiotemporal denoising method of an autocorrelation function, so as to eliminate random noise and interference in the signal and retain the effective features of the original signal. In an optional embodiment, the method for denoising the filtered signal described above can also be used here to implement denoising of each ultrasonic signal.

[0081] Each ultrasonic signal is amplified by the amplifier to enhance the amplitude of the signal, making the characteristics of the signal more obvious and facilitating subsequent processing and analysis.

[0082] By performing filtering, noise reduction and amplification processing on each ultrasonic signal, a processed ultrasonic signal is obtained.

[0083] Step S6: establishing a positioning equation based on the processed ultrasonic signal and the reception time difference of different ultrasonic signals; solving the positioning equation to determine the position of the local discharge source of the transformer.

[0084] In an optional embodiment, establishing a positioning equation based on the processed ultrasonic signal and the reception time difference of different ultrasonic signals includes:

[0085] Get the spatial position of the ultrasonic sensor.

[0086] A positioning equation is established based on the processed ultrasonic signal and the spatial position of the ultrasonic sensor.

[0087] In a preferred embodiment, four ultrasonic sensors are installed on the transformer housing. Assuming that the spatial positions of the four ultrasonic sensors are P1 (x1, y1, z1), P2 (x2, y2, z2), P3 (x3, y3, z3) and P4 (x4, y4, z4), and the spatial position of the partial discharge source is P (x, y, z), the generated positioning equation is as follows:

[0088]

[0089] Wherein, S1 is the Euclidean distance from the spatial position P of the local discharge source to P1; S2 is the Euclidean distance from the spatial position P of the local discharge source to P2; S3 is the Euclidean distance from the spatial position P of the local discharge source to P3; S4 is the Euclidean distance from the spatial position P of the local discharge source to P4;

[0090] The relevant constraints of the positioning equation are as follows:

[0091]

[0092] Among them, t1 is the propagation time of the ultrasonic wave discharged from the local discharge source to P1; t2 is the propagation time of the ultrasonic wave discharged from the local discharge source to P2; t3 is the propagation time of the ultrasonic wave discharged from the local discharge source to P3; t4 is the propagation time of the ultrasonic wave discharged from the local discharge source to P4; Δt1 is the difference between the propagation time of the ultrasonic wave generated by the local discharge source to point P2 and the propagation time of the ultrasonic wave generated by the local discharge source to point P1; Δt2 is the difference between the propagation time of the ultrasonic wave generated by the local discharge source to point P3 and the propagation time of the ultrasonic wave generated by the local discharge source to point P1; Δt3 is the difference between the propagation time of the ultrasonic wave generated by the local discharge source to point P4 and the propagation time of the ultrasonic wave generated by the local discharge source to point P1; v is the propagation velocity of the ultrasonic wave.

[0093] The step of solving the positioning equation to determine the location of the local discharge source of the transformer includes:

[0094] Using the least square method to solve the positioning equation and generate a first positioning result;

[0095] The positioning result is iteratively and robustly estimated using a robust weighted total least squares method until the positioning result meets a preset accuracy requirement, thereby generating a second positioning result;

[0096] The second positioning result is used as the location of the partial discharge source of the transformer.

[0097] It should be explained here that after completing the analysis of the receiving time difference of the ultrasonic signal, the preliminary position of the transformer local discharge source is obtained by solving the positioning equation. Although this initial positioning result can roughly determine the location of the discharge source, it may be affected by factors such as noise interference, measurement error and ultrasonic signal attenuation, resulting in insufficient positioning accuracy.

[0098] In order to further improve the accuracy of the positioning results, the robust weighted total least squares method is used for subsequent processing. This method can iteratively optimize the positioning results while considering the influence of noise, thereby reducing the errors caused by noise and other interference factors.

[0099] The core of the robust weighted least squares method is to assign weights to the measurement results of each sensor. By adjusting the weights, larger weights are given to sensors that are less affected by noise and have higher signal quality, while smaller weights are given to sensor measurements with greater noise, thereby optimizing the overall positioning accuracy.

[0100] In each iteration, the robust weighted least squares method adjusts the weight distribution and error correction value according to the error size of the current positioning result. Each round of iteration gradually approaches the actual discharge source location by minimizing the error residual.

[0101] This process is continuously optimized, and by reducing the positioning error, the influence of the error is gradually weakened, and the accuracy of the discharge source location is improved. As the iteration proceeds, the positioning result will gradually converge to the optimal solution.

[0102] To ensure that the positioning results meet the requirements of practical applications, a preset accuracy threshold is set before the iteration begins as a condition for determining the termination of the iteration. This accuracy threshold can be determined based on specific engineering requirements or historical detection experience, and is usually set to a discharge source location error of millimeters or centimeters.

[0103] After each iteration, the new positioning result is evaluated. If the error of the result is less than the preset accuracy requirement, the iteration process stops and the positioning result is considered to have achieved sufficient accuracy and is set as the final positioning result.

[0104] like Figure 2 As shown, another embodiment of the present invention provides a transformer partial discharge detection method, which, in addition to steps S1 to S6 of the above embodiment, also includes the following steps after step S6:

[0105] Step S7: identifying the type of partial discharge and evaluating the severity of partial discharge according to the characteristic parameters and the location of the partial discharge source; performing trend analysis according to the type of partial discharge and the severity of partial discharge to evaluate the development trend of the transformer insulation condition.

[0106] Specifically, a database of partial discharge types is obtained. The database of partial discharge types is established by analyzing the data accumulation and pattern classification of a large number of partial discharge phenomena in history. The database contains typical characteristics and behavior patterns of different types of partial discharges (such as air gap discharge, surface discharge, surface discharge, etc.). For example, a high-frequency signal may indicate a corona discharge, while a lower-frequency signal may be related to an internal discharge.

[0107] According to the characteristic parameters, by comparing with known patterns in historical data, the current partial discharge signal can be matched with the known characteristics in the historical records, thereby realizing automatic identification of the discharge type.

[0108] The severity of the partial discharge is assessed based on the location of the partial discharge source and the type of the partial discharge.

[0109] It should be explained here that the location of the partial discharge source has an important impact on the severity of the discharge. If the partial discharge source is located near the key components of the transformer (such as windings, insulators or high-voltage leads), it may pose a greater threat to the overall performance of the transformer. Once partial discharge occurs in these key components, it is easy to cause insulation damage or cause more serious electrical failures. For example, if the partial discharge source is located in an area with weak insulation materials or in an environment with poor grounding, the discharge activity may accelerate the aging of the insulation material and increase the risk of failure. Therefore, by analyzing the location of the partial discharge source, its potential risks can be preliminarily judged.

[0110] Different types of partial discharge phenomena usually correspond to different severity and potential damage mechanisms. For example, air gap discharge and surface discharge may have similar discharge signal characteristics, but their effects on insulation are completely different. Air gap discharge may cause arcing and quickly cause insulation breakdown, while surface discharge may not show obvious high-energy discharge in the early stage, but will cause degradation and contamination of the surface of the insulation material, increasing the risk of future failures. By combining the type and location of partial discharge, the severity of partial discharge can be more comprehensively assessed. For example, air gap discharge located near the high-voltage lead may be assessed as high risk, while surface discharge located on the surface of the insulator may be assessed as medium risk in the early stage.

[0111] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0112] like Figure 3 As shown, an embodiment of the present invention provides a transformer partial discharge detection device, including: a pulse current acquisition module 101, a characteristic parameter extraction module 102, a partial discharge judgment module 103, an ultrasonic signal acquisition module 104, a signal processing module 105 and a partial discharge source positioning module 106;

[0113] The pulse current acquisition module 101 is used to acquire the pulse current of the transformer; wherein the pulse current includes the pulse current of the transformer grounding wire and the pulse current of the transformer winding.

[0114] The characteristic parameter extraction module 102 is used to extract a number of characteristic parameters according to the pulse current of the transformer.

[0115] The partial discharge determination module 103 is used to determine whether the transformer has partial discharge according to the characteristic parameters.

[0116] The ultrasonic signal acquisition module 104 is used to acquire a plurality of ultrasonic signals of the transformer when it is determined that partial discharge exists in the transformer.

[0117] The signal processing module 105 is used to perform filtering, noise reduction and amplification processing on each ultrasonic signal to obtain a processed ultrasonic signal.

[0118] The local discharge source positioning module 106 is used to establish a positioning equation based on the processed ultrasonic signal and the reception time difference of different ultrasonic signals, and solve the positioning equation to determine the location of the local discharge source of the transformer.

[0119] In a preferred embodiment, the transformer partial discharge detection device further includes: a partial discharge type determination module 107, a partial discharge severity assessment module 108 and a partial discharge trend analysis module 109;

[0120] The partial discharge type determination module 107 is used to identify the type of partial discharge according to the characteristic parameters and the location of the partial discharge source.

[0121] The partial discharge severity assessment module 108 is used to assess the severity of the partial discharge according to the characteristic parameters and the location of the partial discharge source.

[0122] The partial discharge trend analysis module 109 is used to perform trend analysis according to the type of partial discharge and the severity of partial discharge, and evaluate the development trend of the transformer insulation condition.

[0123] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0124] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0125] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the transformer partial discharge detection method described in any one of the present invention is implemented, or when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0126] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0127] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0128] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0129] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0130] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0131] Another embodiment of the present invention provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute any one of the transformer partial discharge detection methods described above in the present invention.

[0132] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0133] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0134] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A transformer partial discharge detection method, characterized in that: include: Obtaining a pulse current of the transformer; wherein the pulse current includes a pulse current of the transformer grounding wire and a pulse current of the transformer winding; According to the pulse current of the transformer, several characteristic parameters are extracted; According to the several characteristic parameters, determining whether there is a partial discharge phenomenon in the transformer; When it is determined that the transformer has partial discharge, several ultrasonic signals of the transformer are obtained; Performing filtering, noise reduction and amplification processing on each ultrasonic signal to obtain a processed ultrasonic signal; According to the processed ultrasonic signal, a positioning equation is established based on the reception time difference of different ultrasonic signals; and the positioning equation is solved to determine the position of the local discharge source of the transformer.

2. The transformer partial discharge detection method according to claim 1, characterized in that: According to the pulse current of the transformer, several characteristic parameters are extracted, including: Performing filtering on the pulse current to generate a filtering signal; Performing noise reduction processing on the filtered signal to generate a noise reduction signal; amplifying the noise reduction signal to generate an amplified pulse signal; The local discharge signal feature quantity is extracted from the amplified pulse signal to obtain several feature parameters.

3. The transformer partial discharge detection method according to claim 2, characterized in that: The performing noise reduction processing on the filtered signal to generate a noise reduction signal comprises: Calculating an autocorrelation function of the filtered signal; Analyzing the autocorrelation function to identify periodic signals and random noise; The random noise is denoised according to a preset global threshold to generate a denoised signal.

4. The transformer partial discharge detection method according to claim 3, characterized in that: The amplified pulse signal is subjected to partial discharge signal feature extraction to obtain several feature parameters, including: Performing empirical mode decomposition processing on the amplified pulse signal to generate a series of intrinsic mode function components; Each intrinsic mode function component is subjected to singular value decomposition to obtain several characteristic parameters.

5. The transformer partial discharge detection method according to claim 4, characterized in that: The step of solving the positioning equation to determine the location of the partial discharge source of the transformer includes: Using the least square method to solve the positioning equation and generate a first positioning result; The positioning result is iteratively and robustly estimated using a robust weighted total least squares method until the positioning result meets a preset accuracy requirement, thereby generating a second positioning result; The second positioning result is used as the location of the partial discharge source of the transformer.

6. The transformer partial discharge detection method according to claim 5, characterized in that: After solving the positioning equation and determining the location of the partial discharge source of the transformer, the method further includes: identifying the type of partial discharge and assessing the severity of the partial discharge based on the characteristic parameters and the location of the partial discharge source; Perform trend analysis based on the type of partial discharge and the severity of partial discharge to assess the development trend of the transformer insulation condition.

7. A transformer partial discharge detection device, characterized in that: include: Pulse current acquisition module, characteristic parameter extraction module, partial discharge judgment module, ultrasonic signal acquisition module, signal processing module and partial discharge source positioning module; The pulse current acquisition module is used to acquire the pulse current of the transformer; wherein the pulse current includes the pulse current of the transformer grounding wire and the pulse current of the transformer winding; The characteristic parameter extraction module is used to extract a number of characteristic parameters according to the pulse current of the transformer; The partial discharge judgment module is used to judge whether the transformer has partial discharge phenomenon according to the several characteristic parameters; The ultrasonic signal acquisition module is used to acquire a number of ultrasonic signals of the transformer when it is determined that the transformer has a partial discharge phenomenon; The signal processing module is used to perform filtering, noise reduction and amplification processing on each ultrasonic signal to obtain a processed ultrasonic signal; The local discharge source positioning module is used to establish a positioning equation based on the processed ultrasonic signal and the reception time difference of different ultrasonic signals; solve the positioning equation to determine the position of the local discharge source of the transformer.

8. The transformer partial discharge detection device according to claim 7, characterized in that: Also includes: Partial discharge type determination module, partial discharge severity assessment module and partial discharge trend analysis module; The partial discharge type determination module is used to identify the type of partial discharge according to the characteristic parameters and the location of the partial discharge source; The partial discharge severity assessment module is used to assess the severity of the partial discharge according to the characteristic parameters and the location of the partial discharge source; The partial discharge trend analysis module is used to perform trend analysis according to the type of partial discharge and the severity of partial discharge, and evaluate the development trend of the transformer insulation condition.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the transformer partial discharge detection method according to any one of claims 1 to 6 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the transformer partial discharge detection method according to any one of claims 1 to 6 can be implemented.

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