Short-circuit fault detection method and system for transformer windings, and storage medium and device
By acquiring transformer winding leakage magnetic field data, constructing a finite element model and performing simulation, establishing a short-circuit database, and detecting transformer winding short-circuit faults in real time, the accuracy and efficiency problems of transformer winding short-circuit fault diagnosis in existing technologies are solved, ensuring the safe operation of transformers.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
- Filing Date
- 2025-08-19
- Publication Date
- 2026-07-02
Smart Images
Figure CN2025115652_02072026_PF_FP_ABST
Abstract
Description
Transformer winding short-circuit fault detection methods, systems, storage media and equipment Technical Field
[0001] This invention relates to the field of transformer winding detection technology, and in particular to methods, systems, storage media and equipment for detecting short-circuit faults in transformer windings. Background Technology
[0002] Transformers are an indispensable and important piece of equipment in power systems, responsible for the transmission and distribution of electrical energy. However, during long-term operation, transformers may be affected by a variety of factors, such as overvoltage, overcurrent, environmental factors, and equipment aging, which can lead to various failures.
[0003] Short circuit faults are a common and serious type of fault. They can cause overheating of the transformer windings, leading to serious consequences such as damage to insulation materials, fires, and explosions, which seriously affect the safe and stable operation of the power system.
[0004] Currently, the main diagnostic methods for transformer short-circuit faults include manual inspection and local monitoring devices. Manual inspection relies on the experience and skills of the inspectors, which has problems such as strong subjectivity, low efficiency, and easy misjudgment or omission. Although local monitoring devices can monitor the transformer status in specific areas, they cannot fully cover all areas of the transformer, which is prone to missed detection or false detection, and cannot accurately determine the fault location, thus failing to meet the needs of rapid diagnosis. Summary of the Invention
[0005] Therefore, it is necessary to propose a method for detecting short-circuit faults in transformer windings to address the above-mentioned problems.
[0006] A method for detecting short-circuit faults in transformer windings, the method comprising the following steps:
[0007] Obtain leakage flux data between transformer windings;
[0008] The short-circuit fault area is determined based on the leakage magnetic field data;
[0009] Construct a finite element model of the transformer;
[0010] The transformer finite element model is simulated based on the short-circuit fault region to obtain a short-circuit database.
[0011] Real-time data on leakage flux between transformer windings is collected, and the location of short-circuit faults in the transformer windings is determined based on the short-circuit database.
[0012] In the above scheme, obtaining leakage flux data between transformer windings specifically includes:
[0013] Several magnetic sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group;
[0014] The leakage flux between transformer windings is obtained by measuring the leakage flux at all positions between the windings using the sensor group.
[0015] In the above scheme, after obtaining the leakage flux data between transformer windings, the method further includes:
[0016] The collected magnetic flux leakage data undergoes a preprocessing step, which includes noise reduction, filtering, calibration, and standardization.
[0017] In the above scheme, after obtaining the leakage flux data between transformer windings, the method further includes:
[0018] Pattern recognition is performed on the preprocessed leakage magnetic data to determine the operating status of the transformer, which includes normal stable operation, closed state, and winding short-circuit state.
[0019] In the above scheme, determining the short-circuit fault region based on the leakage magnetic field data specifically includes:
[0020] When it is determined that the transformer is in a winding short-circuit state, the preprocessed leakage flux data is arranged according to the change range to obtain a leakage flux change sequence table.
[0021] The short-circuit fault region is determined based on the leakage magnetic flux change sequence table.
[0022] In the above scheme, the step of simulating the transformer finite element model based on the short-circuit fault region to obtain a short-circuit database specifically includes:
[0023] Short-circuit conditions are set at the short-circuit locations within the short-circuit fault region, and transient studies are conducted on them;
[0024] Obtain the leakage flux traversal results of the finite element model of the transformer, including port current waveform, voltage waveform and winding leakage flux data;
[0025] A short-circuit database is constructed based on the port current waveform, voltage waveform, and winding leakage flux data.
[0026] In the above scheme, the real-time acquisition of leakage flux data between transformer windings and the determination of the short-circuit fault location of the transformer windings based on the short-circuit database specifically includes:
[0027] Compare the sensor's magnetic flux leakage data with the magnetic flux leakage traversal results;
[0028] The corresponding fault condition is determined based on the minimum difference.
[0029] The grid-side current and voltage waveform data during a short-circuit fault are compared with the current and voltage data obtained from the finite element model to determine the short-circuit fault point.
[0030] This application also includes a transformer winding short-circuit fault detection system, the system comprising: a leakage flux data acquisition unit, a short-circuit fault determination unit, a simulation model construction unit, and a fault location determination unit;
[0031] The leakage flux data acquisition unit is used to acquire leakage flux data between transformer windings;
[0032] The short-circuit fault determination unit is used to determine the short-circuit fault area based on the leakage magnetic field data;
[0033] The simulation model building unit is used to build a transformer finite element model; and to simulate the transformer finite element model based on the short-circuit fault region to obtain a short-circuit database.
[0034] The fault location determination unit is used to collect leakage magnetic data between transformer windings in real time and determine the short-circuit fault location of the transformer windings based on the short-circuit database.
[0035] This application also includes a readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following steps:
[0036] Obtain leakage flux data between transformer windings;
[0037] The short-circuit fault area is determined based on the leakage magnetic field data;
[0038] Construct a finite element model of the transformer;
[0039] The transformer finite element model is simulated based on the short-circuit fault region to obtain a short-circuit database.
[0040] Real-time data on leakage flux between transformer windings is collected, and the location of short-circuit faults in the transformer windings is determined based on the short-circuit database.
[0041] This application also includes a computer device comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor of the following steps:
[0042] Obtain leakage flux data between transformer windings;
[0043] The short-circuit fault area is determined based on the leakage magnetic field data;
[0044] Construct a finite element model of the transformer;
[0045] The transformer finite element model is simulated based on the short-circuit fault region to obtain a short-circuit database.
[0046] Real-time data on leakage flux between transformer windings is collected, and the location of short-circuit faults in the transformer windings is determined based on the short-circuit database.
[0047] The embodiments of this invention offer the following advantages: First, leakage flux data between transformer windings is acquired; then, short-circuit fault regions are determined based on the leakage flux data; a finite element model of the transformer is constructed; the finite element model is simulated based on the short-circuit fault regions to obtain a short-circuit database; and leakage flux data between transformer windings is collected in real time, and the location of short-circuit faults in the transformer windings is determined based on the short-circuit database. This invention achieves accurate detection of short-circuit faults in transformer windings by acquiring leakage flux data, determining short-circuit fault regions, constructing a finite element model, simulating to obtain a short-circuit database, and performing real-time fault location. This method not only improves the accuracy and efficiency of fault detection but also provides strong protection for the safe operation of transformers. Attached Figure Description
[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0049] in:
[0050] Figure 1 is a schematic flowchart of a transformer winding short-circuit fault detection method in one embodiment. Detailed Implementation
[0051] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0052] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention; however, it will be apparent to those skilled in the art that the invention may be practiced without one or more of these details; in other instances, certain technical features well-known in the art have not been described in order to avoid confusion with the invention. It should be understood that the invention can be practiced in different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided to make the disclosure thorough and complete and to fully convey the scope of the invention to those skilled in the art.
[0053] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms, unless the context clearly indicates otherwise. The terms “comprising” and / or “including,” when used in this specification, identify the presence of said features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.
[0054] To facilitate understanding, the relevant terms used in this application will be introduced below.
[0055] (1) Leakage flux data refers to the magnetic flux data that leaks out from around the core due to magnetic circuit saturation, winding structure defects or external factors during transformer operation. This data can be collected by magnetic sensors installed around the transformer windings.
[0056] (2) The transformer winding is one of the most crucial components of a transformer, responsible for the transmission and conversion of electrical energy. The winding is typically made of copper or aluminum wire and insulated in transformer oil or other insulating materials.
[0057] (3) Transient studies refer to the study of the state changes that a system undergoes in a short period of time, with a focus on the dynamic response process of the system after being subjected to instantaneous disturbances or excitations.
[0058] To fully understand the present invention, a detailed structure will be presented in the following description in order to illustrate the technical solution proposed by the present invention; optional embodiments of the present invention are described in detail below, however, in addition to these detailed descriptions, the present invention may have other embodiments.
[0059] As shown in Figure 1, in one embodiment, a method for detecting short-circuit faults in transformer windings is provided. This method includes steps S101 to S105, which are detailed below:
[0060] S101. Obtain leakage flux data between transformer windings;
[0061] This step is the starting point of the detection method. Through the magnetic sensor group, the magnitude and direction of leakage flux between transformer windings can be measured. The magnitude and direction of leakage flux are related to factors such as winding current, number of turns, and geometry. Therefore, it can reflect the health status of the windings, provide basic data for subsequent fault diagnosis, and provide a basis for judging the fault area and establishing a simulation model.
[0062] In some embodiments, acquiring leakage flux data between transformer windings specifically includes:
[0063] Several magnetic sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group;
[0064] The leakage flux data between transformer windings is obtained by measuring the leakage flux at all locations between the windings using a set of sensors.
[0065] Preferably, the position of a single sensor is designed according to the expected accuracy of leakage flux measurement, so that each single sensor is responsible for monitoring the leakage flux of its corresponding coil. Multiple sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group that can accurately measure the leakage flux at all positions between the windings.
[0066] Furthermore, non-magnetic brackets or clamps are used to fix the sensors to ensure their stable position during operation and to prevent measurement data distortion caused by vibration or other external factors.
[0067] In some embodiments, after acquiring leakage flux data between transformer windings, the method further includes:
[0068] The acquired magnetic flux leakage data undergoes preprocessing steps, including noise reduction, filtering, calibration, and standardization.
[0069] During transformer operation, the data acquisition system is activated to record leakage magnetic field data obtained from various sensors in real time. The collected leakage magnetic field data undergoes necessary preprocessing to improve the reliability and accuracy of the data.
[0070] In some embodiments, after acquiring leakage flux data between transformer windings, the method further includes:
[0071] Pattern recognition is performed on the preprocessed leakage flux data to determine the transformer's operating status, which includes normal stable operation, closed operation, and winding short circuit.
[0072] Specifically, magnetic flux leakage data can be divided into three modes:
[0073] (1) When the transformer is running normally and stably, the effective value of leakage flux remains basically unchanged;
[0074] (2) When the transformer is in the closed state, all sensor data change significantly;
[0075] (3) When a short circuit occurs in the transformer winding, the leakage flux data near the short-circuited winding changes significantly, while the leakage flux at other locations does not change significantly.
[0076] By analyzing the changing trends and patterns of leakage flux data, the operating status of the transformer can be determined. When a short circuit occurs in the winding, the leakage flux data presents pattern (3).
[0077] S102. Determine the short-circuit fault area based on leakage flux data;
[0078] By analyzing leakage flux data, areas of abnormal leakage flux (such as changes in intensity and distribution) can be identified, thereby making a preliminary judgment on the possible areas of short-circuit faults. For example, a short-circuit fault will cause an increase in leakage flux in that area, narrowing the fault diagnosis range, improving diagnosis efficiency, avoiding unnecessary inspection of the entire transformer, and improving the efficiency and accuracy of subsequent steps.
[0079] In some embodiments, determining the short-circuit fault region based on leakage magnetic data specifically includes:
[0080] When it is determined that the transformer is in the winding short-circuit state, the pre-processed leakage flux data is arranged according to the change range to obtain the leakage flux change sequence table.
[0081] The short-circuit fault area is determined based on the sequence table of leakage flux changes.
[0082] S103. Construct a finite element model of the transformer;
[0083] Based on the physical structure and electrical parameters of the transformer, an accurate finite element model is constructed. The finite element model can simulate the magnetic field distribution of the transformer windings, thereby calculating the leakage flux distribution under different fault conditions, providing a foundation for subsequent simulations and the establishment of a short-circuit database.
[0084] Specifically, based on actual transformer parameters, a multi-segment winding finite element model adapted for transformer winding short-circuit fault analysis is established in COMSOL software. The specific steps are as follows: the transformer finite element model is divided into three parts: iron core, winding and tank. The iron core structure is designed according to the form of silicon steel sheet stacking; the winding is divided into multiple pancake units according to the actual pancake structure, and the pancake units are constructed according to the single conductor spiral structure; the tank is simplified to a cuboid structure.
[0085] S104. Simulate the transformer finite element model based on the short-circuit fault region to obtain the short-circuit database;
[0086] By simulating the finite element model, leakage magnetic data can be obtained at different short-circuit fault locations, and a short-circuit database can be established. The database contains leakage magnetic characteristics under different fault states, and a correspondence between short-circuit faults and leakage magnetic data is established, which can be used for subsequent fault diagnosis and provide a reference for actual fault diagnosis. By comparing the real-time collected leakage magnetic data with the database data, the database data that best matches the actual fault situation can be identified, thereby determining the fault location.
[0087] In some embodiments, simulation of the transformer finite element model is performed based on the short-circuit fault region to obtain a short-circuit database, specifically including:
[0088] Short-circuit conditions are set at the short-circuit locations within the short-circuit fault area, and transient studies are conducted on them;
[0089] Obtain the leakage flux ergonomics results of the transformer finite element model. The leakage flux ergonomics results include port current waveforms, voltage waveforms, and winding leakage flux data.
[0090] A short-circuit database is constructed based on port current waveforms, voltage waveforms, and winding leakage flux data.
[0091] S105. Real-time acquisition of leakage magnetic data between transformer windings and determination of short-circuit fault location of transformer windings based on short-circuit database.
[0092] During transformer operation, leakage flux data between windings is collected in real time. The collected leakage flux data is compared and analyzed with data in the short-circuit database to determine the specific location of the short-circuit fault. This enables real-time and accurate detection of short-circuit faults in transformer windings, improving the timeliness and effectiveness of fault handling.
[0093] By comparing the real-time collected leakage magnetic field data with the short-circuit database data, a matching index, such as distance or similarity, can be calculated. The fault location corresponding to the database data with the highest matching degree is the most likely fault location.
[0094] In some embodiments, real-time acquisition of leakage flux data between transformer windings and determination of the short-circuit fault location in the transformer windings based on a short-circuit database specifically includes:
[0095] Compare the leakage magnetic flux data of the sensor with the leakage magnetic flux traversal results;
[0096] The corresponding fault condition is determined based on the minimum difference.
[0097] The grid-side current and voltage waveform data during a short-circuit fault are compared with the current and voltage data obtained from the finite element model to determine the short-circuit fault point.
[0098] Specifically, port current data, voltage data, and leakage flux data are used as the basis for judgment. Port current and voltage data can be obtained through fault recording, while leakage flux data is provided by the magnetic sensor group.
[0099] First, based on the traversal results obtained from the simulation, the leakage magnetic flux data of the sensor is compared with the leakage magnetic flux traversal results of the finite element model. The fault case with the smallest difference is selected for the next step of judgment. Further, the current and voltage waveform data of the grid side during the short circuit fault are compared with the current and voltage data obtained from the finite element model. If the waveforms of the two have a high degree of consistency, the short circuit point is considered to be the correct fault point; otherwise, the data of other fault points in the same area are compared until the short circuit fault point is determined.
[0100] In summary, this invention utilizes the relationship between electromagnetic couplings and employs a magnetic sensor array to monitor leakage magnetic data in real time, comprehensively covering all areas of the transformer and monitoring the possible location of short-circuit faults. This not only accurately determines the short-circuit location but also avoids the uncertainty of subjective judgment in traditional methods, which is of great significance for the safe operation and fault analysis of transformers.
[0101] This application also includes a transformer winding short-circuit fault detection system, the system comprising: a leakage flux data acquisition unit, a short-circuit fault determination unit, a simulation model construction unit, and a fault location determination unit;
[0102] The leakage flux data acquisition unit is used to acquire leakage flux data between transformer windings;
[0103] The short-circuit fault determination unit is used to determine the short-circuit fault area based on leakage flux data.
[0104] The simulation model building unit is used to construct the transformer finite element model; the transformer finite element model is simulated based on the short-circuit fault region to obtain the short-circuit database;
[0105] The fault location determination unit is used to collect leakage magnetic data between transformer windings in real time and determine the short-circuit fault location of the transformer windings based on the short-circuit database.
[0106] This application also includes a readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps:
[0107] Obtain leakage flux data between transformer windings;
[0108] The short-circuit fault area was determined based on the magnetic flux leakage data.
[0109] Construct a finite element model of the transformer;
[0110] Simulation of the transformer finite element model based on the short-circuit fault region was performed to obtain a short-circuit database.
[0111] Real-time acquisition of leakage magnetic data between transformer windings and determination of short-circuit fault location in transformer windings based on short-circuit database.
[0112] This application also includes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor in the following steps:
[0113] Obtain leakage flux data between transformer windings;
[0114] The short-circuit fault area was determined based on the magnetic flux leakage data.
[0115] Construct a finite element model of the transformer;
[0116] Simulation of the transformer finite element model based on the short-circuit fault region was performed to obtain a short-circuit database.
[0117] Real-time acquisition of leakage magnetic data between transformer windings and determination of short-circuit fault location in transformer windings based on short-circuit database.
[0118] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by instructing related hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0119] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0120] The embodiments described above are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application's patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. The embodiments disclosed above are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made according to the claims of this invention are still within the scope of this invention.
Claims
1. A method for detecting short-circuit faults in transformer windings, characterized in that, The method includes: Obtain leakage flux data between transformer windings; The short-circuit fault area is determined based on the leakage magnetic field data; Construct a finite element model of the transformer; The transformer finite element model is simulated based on the short-circuit fault region to obtain a short-circuit database. Real-time data on leakage flux between transformer windings is collected, and the location of short-circuit faults in the transformer windings is determined based on the short-circuit database.
2. The method for detecting short-circuit faults in transformer windings according to claim 1, characterized in that, The acquisition of leakage flux data between transformer windings specifically includes: Several magnetic sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group; The leakage flux between transformer windings is obtained by measuring the leakage flux at all positions between the windings using the sensor group.
3. The method for detecting short-circuit faults in transformer windings according to claim 1, characterized in that, After acquiring the leakage flux data between the transformer windings, the method further includes: The collected magnetic flux leakage data undergoes a preprocessing step, which includes noise reduction, filtering, calibration, and standardization.
4. The method for detecting short-circuit faults in transformer windings according to claim 3, characterized in that, After acquiring the leakage flux data between the transformer windings, the method further includes: Pattern recognition is performed on the preprocessed leakage magnetic data to determine the operating status of the transformer, which includes normal stable operation, closed state, and winding short-circuit state.
5. The method for detecting short-circuit faults in transformer windings according to claim 4, characterized in that, The step of determining the short-circuit fault region based on the leakage magnetic field data specifically includes: When it is determined that the transformer is in a winding short-circuit state, the preprocessed leakage flux data is arranged according to the change range to obtain a leakage flux change sequence table. The short-circuit fault region is determined based on the leakage magnetic flux change sequence table.
6. The method for detecting short-circuit faults in transformer windings according to claim 5, characterized in that, The step of simulating the transformer finite element model based on the short-circuit fault region to obtain a short-circuit database specifically includes: Short-circuit conditions are set at the short-circuit locations within the short-circuit fault region, and transient studies are conducted on them; Obtain the leakage flux traversal results of the finite element model of the transformer, including port current waveform, voltage waveform and winding leakage flux data; A short-circuit database is constructed based on the port current waveform, voltage waveform, and winding leakage flux data.
7. The method for detecting short-circuit faults in transformer windings according to claim 1, characterized in that, The real-time acquisition of leakage flux data between transformer windings and the determination of the short-circuit fault location in the transformer windings based on the short-circuit database specifically includes: Compare the sensor's magnetic flux leakage data with the magnetic flux leakage traversal results; The corresponding fault condition is determined based on the minimum difference. The grid-side current and voltage waveform data during a short-circuit fault are compared with the current and voltage data obtained from the finite element model to determine the short-circuit fault point.
8. A transformer winding short-circuit fault detection system, characterized in that, The system includes: a leakage magnetic field data acquisition unit, a short-circuit fault determination unit, a simulation model construction unit, and a fault location determination unit; The leakage flux data acquisition unit is used to acquire leakage flux data between transformer windings; The short-circuit fault determination unit is used to determine the short-circuit fault area based on the leakage magnetic field data; The simulation model building unit is used to build a transformer finite element model; and to simulate the transformer finite element model based on the short-circuit fault region to obtain a short-circuit database. The fault location determination unit is used to collect leakage magnetic data between transformer windings in real time and determine the short-circuit fault location of the transformer windings based on the short-circuit database.
9. A readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 7.
10. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.