Transformer inter-turn short circuit fault tracing method, storage medium and computer device

Abstract

In the transformer inter-turn short circuit fault tracing method, storage medium and computer device provided in the application, an actual port current waveform when the transformer occurs an inter-turn short circuit fault is obtained; a plurality of estimated short circuit reasons of the transformer sent by a transformer user are received; a transformer finite element model is constructed, the transformer finite element model comprising a winding, the winding comprising a short circuit part and a non-short circuit part; for each estimated short circuit reason, the short circuit part and the non-short circuit part of the winding in the transformer finite element model are modeled in sections according to the estimated short circuit reason, the transformer finite element model after the modeling in sections is used to simulate the estimated short circuit reason, and a simulation port current waveform of each estimated short circuit reason is obtained; a cross-correlation coefficient of the actual port current waveform and the simulation port current waveform of each estimated short circuit reason is calculated, and an inter-turn short circuit fault reason of the transformer is determined according to each cross-correlation coefficient. In this way, the artificial detection cost can be reduced and the finite element model simulation time can be reduced.

Description

Technical Field

[0001] This application relates to the field of power equipment fault diagnosis technology, and in particular to a method for tracing the source of inter-turn short-circuit faults in transformers, a storage medium, and a computer device. Background Technology

[0002] Transformers play a crucial role in power systems, but short-circuit faults often lead to transformer damage and can even seriously threaten the stability and reliability of the system, resulting in significant economic losses. Currently, transformer short-circuit fault analysis mainly employs two methods: factory disassembly and inspection, and finite element model simulation. However, factory disassembly and inspection consumes a large amount of manpower and resources, and is relatively uneconomical and time-consuming. Furthermore, the complex internal structure and precise dimensions of transformers mean that finite element model simulation suffers from the drawbacks of too many possible fault modes, resulting in long simulation times and difficulty in determining the specific short-circuit mode. Blindly analyzing all short-circuit fault modes would consume excessive time and resources. Therefore, in practical research, it is of great significance to find ways to reduce the cost of manual inspection, shorten the simulation time of finite element models, and fully leverage the advantages of both methods. Summary of the Invention

[0003] The purpose of this application is to address at least one of the aforementioned technical deficiencies, particularly the technical deficiencies in existing technologies regarding how to reduce the cost of manual inspection, decrease the simulation time of finite element models, and fully leverage the advantages of both.

[0004] Firstly, this application provides a method for tracing the source of inter-turn short-circuit faults in a transformer, the method comprising:

[0005] Obtain the actual port current waveform when the transformer experiences an inter-turn short circuit fault, and preprocess the actual port current waveform;

[0006] The system receives multiple estimated short-circuit causes for the transformer from the transformer user. These estimated short-circuit causes are obtained by the transformer user based on manual experience and analysis of the pre-processed actual port current waveform.

[0007] Construct a finite element model of a transformer, wherein the finite element model of the transformer includes at least a winding, and the winding includes a short-circuited part and a non-short-circuited part;

[0008] For each of the estimated short-circuit causes, the short-circuit and non-short-circuit portions of the winding in the transformer finite element model are segmented and modeled according to the estimated short-circuit cause. The transformer finite element model after segmentation is then used to simulate the estimated short-circuit cause to obtain the simulated port current waveform corresponding to each of the estimated short-circuit causes.

[0009] Calculate the cross-correlation coefficient between the actual port current waveform and the simulated port current waveform corresponding to each of the estimated short-circuit causes, and determine the cause of the inter-turn short-circuit fault of the transformer based on each cross-correlation coefficient.

[0010] In one embodiment, the step of preprocessing the actual port current waveform includes:

[0011] The actual port current waveform is subjected to noise reduction, filtering, sampling frequency adjustment, and timing analysis.

[0012] In one embodiment, the step of segmenting and modeling the short-circuited and non-short-circuited portions of the winding in the transformer finite element model according to each estimated short-circuit cause includes:

[0013] For each of the predicted short circuit causes, if the predicted short circuit cause involves a pie-scale short circuit, the modeling method for the short-circuited portion of the winding is to establish a winding model by dividing the pie, and the modeling method for the non-short-circuited portion of the winding is to maintain the overall modeling. If the predicted short circuit cause involves an inter-turn short circuit within the pie, the modeling method for the short-circuited portion of the winding is to establish a winding model by dividing the turns, and the modeling method for the non-short-circuited portion of the winding is to maintain the overall modeling.

[0014] In one embodiment, the step of determining the cause of the inter-turn short-circuit fault of the transformer based on each of the cross-correlation coefficients includes:

[0015] For each cross-correlation number, if the cross-correlation number is greater than a preset threshold, the estimated short-circuit cause corresponding to the cross-correlation number is taken as the short-circuit cause to be analyzed.

[0016] The causes of the short circuits to be analyzed are determined to be the causes of the inter-turn short circuit faults in the transformer.

[0017] In one embodiment, the transformer finite element model also includes an iron core, the structure of which is a stack of silicon steel sheets.

[0018] In one embodiment, the transformer finite element model also includes an oil tank, the oil tank having a cuboid structure.

[0019] Secondly, this application provides a transformer inter-turn short-circuit fault tracing device, the device comprising:

[0020] The actual port current waveform acquisition module is used to acquire the actual port current waveform when the transformer experiences an inter-turn short circuit fault, and to preprocess the actual port current waveform.

[0021] The short circuit cause receiving module is used to receive multiple estimated short circuit causes corresponding to the transformer sent by the transformer user. The estimated short circuit causes are obtained by the transformer user based on manual experience and analysis of the pre-processed actual port current waveform.

[0022] A transformer finite element model construction module is used to construct a transformer finite element model, which includes at least a winding, and the winding includes a short-circuited part and a non-short-circuited part;

[0023] The simulation port current waveform acquisition module is used to, for each of the estimated short circuit causes, segment the short-circuited part and the non-short-circuited part of the winding in the transformer finite element model according to the estimated short circuit cause, and use the segmented transformer finite element model to simulate the estimated short circuit cause, so as to obtain the simulation port current waveform corresponding to each of the estimated short circuit causes.

[0024] The inter-turn short-circuit fault cause determination module is used to calculate the cross-correlation coefficient between the actual port current waveform and the simulated port current waveform for each of the estimated short-circuit causes, and determine the cause of the inter-turn short-circuit fault of the transformer based on each cross-correlation coefficient.

[0025] In one embodiment, the actual port current waveform acquisition module includes:

[0026] The actual port current waveform preprocessing unit is used to perform noise reduction, filtering, sampling frequency adjustment and timing analysis on the actual port current waveform.

[0027] Thirdly, this application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the transformer inter-turn short-circuit fault tracing method as described in any of the above embodiments.

[0028] Fourthly, this application provides a computer device, including: one or more processors, and a memory;

[0029] The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the transformer inter-turn short-circuit fault tracing method as described in any of the above embodiments.

[0030] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0031] The transformer inter-turn short-circuit fault tracing method, storage medium, and computer equipment provided in this application acquire the actual port current waveform when an inter-turn short-circuit fault occurs in the transformer and preprocess the actual port current waveform; receive multiple estimated short-circuit causes corresponding to the transformer sent by the transformer user, the estimated short-circuit causes being obtained by the transformer user based on manual experience and analysis of the preprocessed actual port current waveform; construct a transformer finite element model, the transformer finite element model including at least windings, the windings including short-circuit parts and non-short-circuit parts; for each estimated short-circuit cause, according to the estimated short-circuit cause, segment the short-circuit part and non-short-circuit part of the winding in the transformer finite element model are modeled, and the estimated short-circuit cause is simulated using the segmented modeled transformer finite element model to obtain the simulated port current waveform corresponding to each estimated short-circuit cause; calculate the cross-correlation coefficient between the actual port current waveform and the simulated port current waveform for each estimated short-circuit cause, and determine the cause of the transformer inter-turn short-circuit fault based on each cross-correlation coefficient. This method reduces reliance on manpower and resources, thus lowering the cost of manual inspection, by acquiring transformer port current waveforms and constructing a transformer finite element model. In the transformer finite element model, the windings are divided into short-circuited and non-short-circuited parts. During simulation, the short-circuited and non-short-circuited parts of the windings can be modeled segmentally based on the predicted short-circuit causes, making the segmented finite element model closer to reality, thereby improving simulation accuracy and reducing simulation time. Using pre-processed actual port current waveforms and simulated port current waveforms corresponding to various predicted short-circuit causes allows for rapid simulation calculations, reducing simulation time. Combining actual port current waveforms with finite element model simulation fully leverages the advantages of both, allowing for both acquisition of actual port current waveforms and simulation calculations using the finite element model, thus providing a more comprehensive analysis of inter-turn short-circuit faults in the transformer. By calculating the cross-correlation coefficients between the actual port current waveforms and each simulated port current waveform, the causes of inter-turn short-circuit faults in the transformer can be determined, leading to more accurate fault analysis and improved fault analysis accuracy. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating the transformer inter-turn short-circuit fault tracing method provided in this application embodiment;

[0034] Figure 2This is a schematic diagram of the structure of the transformer inter-turn short-circuit fault tracing device provided in the embodiments of this application;

[0035] Figure 3 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] This application provides a method for tracing the source of inter-turn short-circuit faults in transformers. The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device can be any device with data processing capabilities, including but not limited to a single server, server cluster, personal laptop, desktop computer, etc. Figure 1 As shown, this application provides a method for tracing the source of inter-turn short-circuit faults in transformers, the method comprising:

[0038] S101: Obtain the actual port current waveform when the transformer experiences an inter-turn short-circuit fault, and preprocess the actual port current waveform.

[0039] Inter-turn short-circuit faults refer to the situation where a short circuit occurs between the coils of a transformer, meaning that two or more turns of the coil have come into contact or are short-circuited. Port current waveforms refer to the graphs showing the change in current over time at different ports (leads connecting the coils) of the transformer. Actual port current waveforms are the actual measured values ​​of the current at each port when an inter-turn short-circuit fault occurs. Preprocessing involves a series of operations on the actual port current waveforms to extract useful information and reduce the impact of noise.

[0040] In this step, the actual port current waveform when an inter-turn short-circuit fault occurs in the transformer can be acquired first. In one example, the current at different ports of the transformer can be collected and recorded in real time using current sensors connected to the ports or using internal measuring devices within the transformer. Specific preprocessing operations can include filtering, baseline drift removal, and peak detection, with the aim of obtaining more accurate and reliable data for subsequent analysis and processing. Filtering can utilize low-pass filters or other filtering techniques to remove high-frequency noise and interference signals. If baseline drift exists in the actual port current waveform, it can be corrected using differential operations or curve fitting to make the waveform relatively stable. Peak detection can detect peaks or extreme points in the waveform. Data normalization can normalize the current values ​​to make the data from different ports comparable. Furthermore, the preprocessed actual port current waveform data can be recorded and stored.

[0041] S102: Receive multiple estimated short-circuit causes corresponding to the transformer sent by the transformer user. The estimated short-circuit causes are obtained by the transformer user based on manual experience and analysis of the pre-processed actual port current waveform.

[0042] In this step, based on the transformer user's experience and the pre-processed actual port current waveform, the system can receive multiple estimated short-circuit causes sent by the transformer user. In one example, the user can provide a set of possible short-circuit causes, such as: damaged inter-turn insulation (caused by aging insulation materials, mechanical damage, or overvoltage in the transformer coil); foreign object ingress (external impurities such as dust, moisture, or foreign objects entering the transformer, causing an inter-turn short circuit); loose winding connections (caused by loose or corroded connections in the transformer windings); overload or overvoltage (long-term overload operation or excessively high voltage input may lead to inter-turn short circuits in the transformer windings); and winding design or manufacturing defects (defects in the design or manufacturing process of the transformer windings increase the likelihood of inter-turn short circuits).

[0043] In one example, when a user determines the estimated cause of a transformer short circuit, the faulty transformer can be returned to the factory or disassembled and analyzed on-site. First, a visual inspection is performed, carefully examining the exterior of the transformer for any visible damage, burns, or abnormal signs. Next, an internal inspection is conducted, opening the transformer and inspecting internal components, especially the windings and core, for any signs of thermal damage, short circuits, breakdowns, or other damage. Finally, based on the user's personal maintenance experience and the analysis of the pre-processed actual port current waveform, the estimated cause of the transformer short circuit is determined.

[0044] S103: Construct a finite element model of a transformer, wherein the finite element model of the transformer includes at least a winding, and the winding includes a short-circuited portion and a non-short-circuited portion.

[0045] Among them, the finite element model is a mathematical modeling method used to simulate and analyze the physical phenomena of complex structures.

[0046] In this step, when constructing the finite element model of the transformer, the geometric structure of the model can be determined first based on the actual transformer structure. This model must include at least the windings, as it is used for tracing short circuits between transformer turns. Therefore, the windings can be divided into short-circuited and non-short-circuited sections for segmented modeling, making the improved finite element model closer to reality, thereby improving simulation accuracy and reducing simulation time. Next, the model can be divided into several small elements, each with the same physical characteristics. These small elements will form a finite element mesh, used to calculate parameters such as electric field, magnetic field, and current during the simulation. Then, based on the model's geometric structure and physical characteristics, a mathematical model is established and solved. The model needs to consider electromagnetic phenomena in the transformer, including induced current, magnetic flux, eddy currents, etc. Finally, the physical model can be solved using the finite element method to obtain the required electric field, magnetic field, and current parameters. By analyzing and processing these parameters, the operating state and characteristics of the transformer can be understood. It is understandable that finite element software such as COMSOL can be used to build the finite element model of the transformer.

[0047] S104: For each of the estimated short-circuit causes, the short-circuit part and the non-short-circuit part of the winding in the transformer finite element model are segmented and modeled according to the estimated short-circuit cause. The transformer finite element model after segmentation is used to simulate the estimated short-circuit cause to obtain the simulated port current waveform corresponding to each of the estimated short-circuit causes.

[0048] In this step, during the simulation of each predicted short-circuit cause using a transformer finite element model, the simulation conditions are first determined based on the predicted short-circuit cause, including loading conditions, boundary conditions, initial conditions, etc. Then, the short-circuited and non-short-circuited portions of the transformer windings in the finite element model are modeled separately, that is, based on the predicted cause, the short-circuited and non-short-circuited portions are modeled separately. For example, the short-circuited portion can be modeled as a loop with low resistance and high conductance. By reducing the resistance and increasing the conductance, the large current flowing through this part of the winding caused by the short circuit can be simulated. No special treatment is needed for the non-short-circuited portion. The predicted short-circuit cause is simulated using the segmented transformer finite element model. In the segmented transformer finite element model, electromagnetic phenomena in the transformer need to be considered, including induced current, magnetic flux, eddy currents, etc. Based on the predicted short-circuit cause, corresponding defects or faults can be introduced into the model. The physical model is solved using the finite element method to obtain the required electric field, magnetic field, current, and other parameters. By analyzing and processing these parameters, we can understand the simulated port current waveform corresponding to each predicted short circuit cause.

[0049] S105: Calculate the cross-correlation coefficient between the actual port current waveform and the simulated port current waveform for each of the estimated short-circuit causes, and determine the cause of the inter-turn short-circuit fault of the transformer based on each cross-correlation coefficient.

[0050] In this step, for each short-circuit cause, a cross-correlation analysis is performed between the actual port current waveform and the corresponding simulated port current waveform. The cross-correlation coefficient reflects the similarity between the two signals, with values ​​ranging from -1 to 1. By comparing the cross-correlation coefficients corresponding to each short-circuit cause, the simulated port current waveform with the highest correlation can be selected as the most likely cause of the inter-turn short-circuit fault.

[0051] In the above embodiments, the actual port current waveform when an inter-turn short-circuit fault occurs in the transformer is acquired and preprocessed; multiple estimated short-circuit causes corresponding to the transformer are received from the transformer user, which are obtained by the transformer user based on manual experience and analysis of the preprocessed actual port current waveform; a transformer finite element model is constructed, which includes at least windings, including short-circuit and non-short-circuit portions; for each estimated short-circuit cause, the short-circuit and non-short-circuit portions of the windings in the transformer finite element model are segmented and modeled according to the estimated short-circuit cause, and the estimated short-circuit cause is simulated using the improved segmented modeling transformer finite element model to obtain the simulated port current waveform corresponding to each estimated short-circuit cause; the cross-correlation coefficient between the actual port current waveform and each simulated port current waveform is calculated, and the cause of the inter-turn short-circuit fault of the transformer is determined based on each cross-correlation coefficient. This method reduces reliance on manpower and resources, thus lowering the cost of manual inspection, by acquiring transformer port current waveforms and constructing a transformer finite element model. In the transformer finite element model, the windings are divided into short-circuited and non-short-circuited parts. During simulation, the short-circuited and non-short-circuited parts of the windings can be modeled segmentally based on the predicted short-circuit causes, making the segmented finite element model closer to reality, thereby improving simulation accuracy and reducing simulation time. Using pre-processed actual port current waveforms and simulated port current waveforms corresponding to various predicted short-circuit causes allows for rapid simulation calculations, reducing simulation time. Combining actual port current waveforms with finite element model simulation fully leverages the advantages of both, allowing for both acquisition of actual port current waveforms and simulation calculations using the finite element model, thus providing a more comprehensive analysis of inter-turn short-circuit faults in the transformer. By calculating the cross-correlation coefficients between the actual port current waveforms and each simulated port current waveform, the causes of inter-turn short-circuit faults in the transformer can be determined, leading to more accurate fault analysis and improved fault analysis accuracy.

[0052] In one embodiment, the step of preprocessing the actual port current waveform includes:

[0053] The actual port current waveform is subjected to noise reduction, filtering, sampling frequency adjustment, and timing analysis.

[0054] Specifically, actual port current waveforms may be affected by various noises, such as power supply noise and measurement noise. Noise reduction algorithms can be used to process the waveform to reduce the impact of noise and extract the effective signal. Commonly used noise reduction methods include mean filtering, median filtering, and wavelet denoising. Actual port current waveforms may contain high-frequency or low-frequency components. For ease of analysis and processing, filters can be applied to filter the waveform. Appropriate filter types (such as low-pass, high-pass, band-pass, or band-stop) and cutoff frequencies should be selected as needed to preserve the frequency range of interest. The sampling frequency of the actual port current waveform may differ from that of the simulated port current waveform. To perform cross-correlation analysis, the sampling frequencies of both need to be adjusted to be consistent. Interpolation or decimation techniques can be used to adjust the sampling frequency of the waveform to achieve the same sampling frequency. For actual port current waveforms, time-series analysis can be performed, such as waveform display, waveform averaging, peak detection, and spectrum analysis, to better understand waveform characteristics, extract useful information, and prepare for subsequent cross-correlation analysis.

[0055] Furthermore, time-series analysis is performed on the actual port current waveform to understand the time evolution of the fault and identify the fault point. If multiple consecutive short circuits occur, it is necessary to analyze the situation of each short circuit, including the time, location and type of the short circuit, and to make a preliminary judgment on the short circuit development process.

[0056] In one embodiment, the step of segmenting and modeling the short-circuited and non-short-circuited portions of the winding in the transformer finite element model according to each estimated short-circuit cause includes:

[0057] For each of the predicted short circuit causes, if the predicted short circuit cause involves a pie-scale short circuit, the modeling method for the short-circuited portion of the winding is to establish a winding model by dividing the pie, and the modeling method for the non-short-circuited portion of the winding is to maintain the overall modeling. If the predicted short circuit cause involves an inter-turn short circuit within the pie, the modeling method for the short-circuited portion of the winding is to establish a winding model by dividing the turns, and the modeling method for the non-short-circuited portion of the winding is to maintain the overall modeling.

[0058] Specifically, for each predicted short-circuit cause, the modeling method for the short-circuited and non-short-circuited parts of the transformer winding in the finite element model can be determined based on the predicted cause. If the predicted short-circuit cause involves a pie-scale short circuit, the modeling method for the short-circuited part of the winding is to establish a pie-scale winding model. This model treats the entire winding as a single pie, with each turn on the same level and no overlap between turns. Therefore, this model ignores the interaction between different turns and only considers parameters such as the total inductance and total resistance of the entire winding. If the predicted short-circuit cause involves an inter-turn short circuit within the pie, the modeling method for the short-circuited part of the winding is to establish a turn-by-turn winding model. This model treats the entire winding as composed of multiple independent turns, each with its own inductance, resistance, and mutual inductance values. There are overlaps between turns. This model is more refined and can more accurately describe the behavior of inductors or transformers in low-frequency circuits. In cases involving short circuits at the pie scale or between turns within the pie, the un-short-circuited portion of the winding is modeled holistically, treating it as a single unit without special processing. A segmented finite element model of the transformer is then used for simulation. During the simulation, the finite element method (FEA) or other suitable simulation methods can be used to calculate the port current waveform response under given short-circuit conditions. The simulated port current waveforms corresponding to each predicted short-circuit cause are obtained through simulation calculations. The simulated port current waveforms reflect the current distribution and response within the transformer under different short-circuit conditions.

[0059] In this embodiment, for the short-circuit section, using a segmented winding model or a turn-by-turn model can more accurately describe the short-circuit path and current distribution, improving the model's accuracy and predicting circuit behavior more precisely. By simulating each predicted short-circuit cause, corresponding simulated port current waveforms can be obtained. These simulated port current waveforms provide detailed information about the transformer's current distribution and response under different short-circuit fault conditions. Comparison and analysis with actual port current waveforms can help determine the true cause of the transformer fault and enable accurate fault diagnosis.

[0060] In one embodiment, the step of determining the cause of the inter-turn short-circuit fault of the transformer based on each of the cross-correlation coefficients includes:

[0061] For each cross-correlation number, if the cross-correlation number is greater than a preset threshold, the estimated short-circuit cause corresponding to the cross-correlation number is taken as the short-circuit cause to be analyzed.

[0062] The causes of the short circuits to be analyzed are determined to be the causes of the inter-turn short circuit faults in the transformer.

[0063] Specifically, the preset threshold can be determined based on the actual situation. For each cross-correlation coefficient, if the cross-correlation coefficient is greater than the preset threshold, the estimated short-circuit cause corresponding to that cross-correlation coefficient is taken as the short-circuit cause to be analyzed. In-depth analysis of each short-circuit cause can be conducted using signal processing methods, feature extraction, machine learning, and other techniques, combined with the experience of domain experts, to arrive at the final cause of the transformer inter-turn short-circuit fault.

[0064] In this embodiment, cross-correlation coefficients are used for analysis, eliminating the need for manual screening of possible short-circuit causes. This method enables automated processing, improves fault detection efficiency, and reduces the risk of human error. Cross-correlation coefficients are a reliable statistical method that, through analysis of transformer winding current signals, yields the correlation between different short-circuit causes. Based on the magnitude of the correlation, the probability of each predicted short-circuit cause can be quantitatively assessed, thereby improving the accuracy of fault diagnosis.

[0065] In one embodiment, the finite element model of the transformer further includes an iron core, the structure of which is a stack of silicon steel sheets.

[0066] Specifically, stacked silicon steel sheets are a structural form of power transformer cores, exhibiting lower iron losses and eddy current losses, making them suitable for high-efficiency power transmission and distribution. Typically, the stacked silicon steel sheet core structure can effectively reduce hysteresis losses in the core, thereby improving transformer efficiency.

[0067] In one embodiment, the transformer finite element model further includes an oil tank, the oil tank having a cuboid structure.

[0068] Specifically, the finite element model of a transformer includes the oil tank, which can be simplified as a cuboid.

[0069] In one example, the fuel tank can be geometrically modeled using a finite element mesh, representing a cuboid structure, or alternatively, a cube structure. When modeling the fuel tank, its length, width, and height, as well as any possible internal baffles or support structures, can be considered. Fuel tanks are typically made of metallic materials (such as steel plates), therefore, material properties such as the elastic modulus, Poisson's ratio, and density need to be defined to accurately describe its mechanical behavior in the finite element analysis. If the fuel tank has contact or connections with other parts (such as windings or the core), these interface conditions need to be appropriately modeled to accurately describe the interaction between the fuel tank and other parts. In actual operation, the fuel tank may be subjected to mechanical loads (such as vibration and pressure), therefore, these mechanical loads also need to be appropriately modeled and considered in the finite element model. The fuel tank's support method, fixed constraints, or free constraints, and other boundary conditions also need to be defined in the finite element model to ensure the model's realism and accuracy.

[0070] In this embodiment, modeling the fuel tank as a cuboid structure simplifies the finite element modeling process and reduces model complexity. Compared to completely preserving the actual geometry of the fuel tank, using a cuboid structure allows for faster and more efficient modeling and meshing. The regular geometry of the cuboid structure makes meshing easier, reducing the number of computational cells, saving computational resources (such as memory and computation time), and improving the efficiency of finite element analysis.

[0071] The transformer inter-turn short-circuit fault tracing device provided in the embodiments of this application is described below. The transformer inter-turn short-circuit fault tracing device described below can be referred to in correspondence with the transformer inter-turn short-circuit fault tracing method described above. Figure 2 As shown, this application provides a transformer inter-turn short-circuit fault tracing device, the device comprising:

[0072] The actual port current waveform acquisition module 201 is used to acquire the actual port current waveform when the transformer experiences an inter-turn short circuit fault, and to preprocess the actual port current waveform.

[0073] The short circuit cause receiving module 202 is used to receive multiple estimated short circuit causes corresponding to the transformer sent by the transformer user. The estimated short circuit causes are obtained by the transformer user based on manual experience and analysis of the pre-processed actual port current waveform.

[0074] The transformer finite element model construction module 203 is used to construct a transformer finite element model, which includes at least a winding, and the winding includes a short-circuited part and a non-short-circuited part;

[0075] The simulation port current waveform acquisition module 204 is used to, for each of the estimated short circuit causes, segment the short-circuited part and the non-short-circuited part of the winding in the transformer finite element model according to the estimated short circuit cause, and use the segmented transformer finite element model to simulate the estimated short circuit cause, so as to obtain the simulation port current waveform corresponding to each of the estimated short circuit causes.

[0076] The inter-turn short circuit fault cause determination module 205 is used to calculate the cross-correlation coefficient between the actual port current waveform and the simulated port current waveform corresponding to each of the estimated short circuit causes, and determine the cause of the inter-turn short circuit fault of the transformer based on each cross-correlation coefficient.

[0077] In one embodiment, the actual port current waveform acquisition module 201 includes:

[0078] The actual port current waveform preprocessing unit is used to perform noise reduction, filtering, sampling frequency adjustment and timing analysis on the actual port current waveform.

[0079] In one embodiment, the simulation port current waveform acquisition module 204 includes:

[0080] The simulation port current waveform acquisition unit is used to, for each of the estimated short circuit causes, if the estimated short circuit cause involves a pie-scale short circuit, then the modeling method for the short-circuited part of the winding is to establish a winding model by dividing the pie, and the modeling method for the non-short-circuited part of the winding is to maintain the overall modeling; if the estimated short circuit cause involves an inter-turn short circuit within the pie, then the modeling method for the short-circuited part of the winding is to establish a winding model by dividing the turns, and the modeling method for the non-short-circuited part of the winding is to maintain the overall modeling.

[0081] In one embodiment, the inter-turn short-circuit fault cause determination module 205 includes:

[0082] The short circuit cause determination unit is used to determine the short circuit cause to be analyzed for each cross-correlation number if the cross-correlation number is greater than a preset threshold.

[0083] The inter-turn short-circuit fault cause determination unit is used to analyze each of the short-circuit causes to be analyzed, and to obtain the cause of the inter-turn short-circuit fault of the transformer.

[0084] In one embodiment, the core is constructed of stacked silicon steel sheets.

[0085] In one embodiment, the transformer finite element model further includes an oil tank, the oil tank having a cuboid structure.

[0086] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the transformer inter-turn short-circuit fault tracing method as described in any of the above embodiments.

[0087] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the transformer inter-turn short-circuit fault tracing method as described in any of the above embodiments.

[0088] Indicatively, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 3The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the transformer inter-turn short-circuit fault tracing method of any of the above embodiments.

[0089] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0090] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0091] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0092] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0093] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for tracing the source of inter-turn short-circuit faults in a transformer, characterized in that, The method includes: Obtain the actual port current waveform when the transformer experiences an inter-turn short circuit fault, and preprocess the actual port current waveform; The system receives multiple estimated short-circuit causes for the transformer from the transformer user. These estimated short-circuit causes are obtained by the transformer user based on manual experience and analysis of the pre-processed actual port current waveform. Construct a finite element model of a transformer, wherein the finite element model of the transformer includes at least a winding, and the winding includes a short-circuited part and a non-short-circuited part; For each of the estimated short-circuit causes, if the estimated short-circuit cause involves a pie-scale short circuit, the modeling method for the short-circuited part of the winding is to establish a winding model by dividing the pie, and the modeling method for the non-short-circuited part of the winding is to maintain the overall modeling. If the estimated short-circuit cause involves an inter-turn short circuit within the pie, the modeling method for the short-circuited part of the winding is to establish a winding model by dividing the turns, and the modeling method for the non-short-circuited part of the winding is to maintain the overall modeling. The estimated short-circuit cause is simulated using the segmented modeled transformer finite element model to obtain the simulated port current waveform corresponding to each of the estimated short-circuit causes. Calculate the cross-correlation coefficient between the actual port current waveform and the simulated port current waveform corresponding to each of the estimated short-circuit causes, and determine the cause of the inter-turn short-circuit fault of the transformer based on each cross-correlation coefficient.

2. The method for tracing the source of inter-turn short-circuit faults in transformers according to claim 1, characterized in that, The step of preprocessing the actual port current waveform includes: The actual port current waveform is subjected to noise reduction, filtering, sampling frequency adjustment, and timing analysis.

3. The method for tracing the source of inter-turn short-circuit faults in transformers according to claim 1, characterized in that, The step of determining the cause of the inter-turn short-circuit fault of the transformer based on each of the cross-correlation coefficients includes: For each cross-correlation number, if the cross-correlation number is greater than a preset threshold, the estimated short-circuit cause corresponding to the cross-correlation number is taken as the short-circuit cause to be analyzed. The causes of the short circuits to be analyzed are determined to be the causes of the inter-turn short circuit faults in the transformer.

4. The method for tracing the source of inter-turn short-circuit faults in transformers according to any one of claims 1 to 3, characterized in that, The transformer finite element model also includes an iron core, the structure of which is a stack of silicon steel sheets.

5. The method for tracing the source of inter-turn short-circuit faults in transformers according to any one of claims 1 to 3, characterized in that, The transformer finite element model also includes an oil tank, which has a cuboid structure.

6. A transformer inter-turn short-circuit fault tracing device, characterized in that, The device includes: The actual port current waveform acquisition module is used to acquire the actual port current waveform when the transformer experiences an inter-turn short circuit fault, and to preprocess the actual port current waveform. The short circuit cause receiving module is used to receive multiple estimated short circuit causes corresponding to the transformer sent by the transformer user. The estimated short circuit causes are obtained by the transformer user based on manual experience and analysis of the pre-processed actual port current waveform. A transformer finite element model construction module is used to construct a transformer finite element model, which includes at least a winding, and the winding includes a short-circuited part and a non-short-circuited part; The simulation port current waveform acquisition module is used to simulate the estimated short circuit cause for each of the estimated short circuit causes. If the estimated short circuit cause involves a pie-scale short circuit, the modeling method for the short-circuited part of the winding is to establish a winding model by dividing the pie, and the modeling method for the non-short-circuited part of the winding is to maintain the overall modeling. If the estimated short circuit cause involves an inter-turn short circuit within the pie, the modeling method for the short-circuited part of the winding is to establish a winding model by dividing the turns, and the modeling method for the non-short-circuited part of the winding is to maintain the overall modeling. The module then uses the segmented modeled transformer finite element model to simulate the estimated short circuit cause to obtain the simulation port current waveform corresponding to each of the estimated short circuit causes. The inter-turn short-circuit fault cause determination module is used to calculate the cross-correlation coefficient between the actual port current waveform and the simulated port current waveform corresponding to each of the estimated short-circuit causes, and determine the cause of the inter-turn short-circuit fault of the transformer based on each cross-correlation coefficient.

7. The transformer inter-turn short-circuit fault tracing device according to claim 6, characterized in that, The actual port current waveform acquisition module includes: The actual port current waveform preprocessing unit is used to perform noise reduction, filtering, sampling frequency adjustment and timing analysis on the actual port current waveform.

8. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the transformer inter-turn short-circuit fault tracing method as described in any one of claims 1 to 5.

9. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the transformer inter-turn short-circuit fault tracing method as described in any one of claims 1 to 5.

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