A method for diagnosing winding deformation of a transformer under short circuit fault

Abstract

The application discloses a kind of winding deformation diagnostic methods under transformer short-circuit fault, belong to transformer fault diagnosis field.It includes: development constructs three-dimensional electromagnetic structure finite element model, based on three-dimensional electromagnetic structure finite element model simulation short-circuit condition magnetic field and electromagnetic force interaction, establish the nonlinear mapping relationship between leakage magnetic field distribution and mechanical deformation;Through magnetic sensor array capture dynamic leakage magnetic field change on transformer oil tank;Based on least square magnetic field inversion algorithm, the magnetic flux density is associated with deformation amplitude, and the winding deformation is diagnosed to obtain the diagnostic result.The application realizes the quick and accurate diagnosis of winding deformation under transformer short-circuit fault by integrating multi-physical field modeling, optimized sensor array and advanced algorithm, which provides a strong guarantee for the reliable operation of smart grid.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of transformer fault diagnosis, and particularly relates to a winding deformation diagnosis method under transformer short-circuit fault. BACKGROUND

[0002] Power transformers are the key equipment for power transmission and voltage conversion in power grids, and their short-circuit resistance is determined by the mechanical stability of the winding. The winding deformation during a short-circuit fault of a power transformer affects the integrity of the equipment and the reliability of the power grid. During an SC fault, the transient current generates strong electromagnetic forces, which produce mechanical stresses exceeding the yield strength of the insulation material, leading to irreversible damage to the winding, such as inter-turn insulation failure, conductor misplacement, and axial collapse. However, traditional diagnosis methods face great limitations. Dissolved gas analysis (DGA) detects discharges or overheating by monitoring characteristic gases (such as C2H2, H2), but gas diffusion delays and multi-field interference hinder accurate deformation quantification and localization. Frequency response analysis (FRA) identifies structural abnormalities through frequency-domain transfer functions (10 Hz to 2 MHz), but its accuracy is affected by capacitive effects and ground interference, requiring offline testing, which is not suitable for dynamic monitoring. Similarly, the short-circuit impedance method (SCIM) assesses deformation through changes in leakage inductance, but relies on manual offline procedures and cannot capture transient SC responses. SUMMARY

[0003] To solve the above problems, the application provides a winding deformation diagnosis method under transformer short-circuit fault, comprising:

[0004] Developing a three-dimensional electromagnetic structure finite element model, simulating the interaction of magnetic fields and electromagnetic forces under short-circuit conditions based on the three-dimensional electromagnetic structure finite element model, and establishing a nonlinear mapping relationship between leakage magnetic field distribution and mechanical deformation;

[0005] Capturing dynamic leakage magnetic field changes on the transformer oil tank through a magnetic sensor array;

[0006] Based on the least squares magnetic field inversion algorithm, the magnetic flux density is associated with the deformation amplitude, the winding deformation is diagnosed, and the diagnosis result is obtained.

[0007] Preferably, the three-dimensional electromagnetic structure finite element model is developed based on a 110 kV transformer, simulating the electromagnetic-mechanical interaction of the winding under short-circuit fault conditions.

[0008] Preferably, the process of establishing a nonlinear mapping relationship between leakage magnetic field distribution and mechanical deformation comprises:

[0009] The electromagnetic force and short-circuit impact current under short-circuit fault are obtained, based on a three-dimensional electromagnetic structure finite element model, combined with the finite element discretization principle, the space-time distribution law of the magnetic flux density under the action of transient short-circuit current is obtained, and the dynamic electromagnetic force characteristics are iteratively calculated combined with the electromagnetic force formula.

[0010] Preferably, the electromagnetic force The formula expression is:

[0011] ;

[0012] In the formula, L is the effective conductor length, I is the current density, and B is the magnetic induction value;

[0013] According to the Biot-Savart law, the magnetic induction value B is expressed by the current I as:

[0014] ;

[0015] In the formula, μ is the magnetic permeability, dl is the differential current element vector of the source, e r is the unit vector of the current element pointing to the field point, and r is the straight-line distance between the current element and the observation point.

[0016] Preferably, the formula expression of the short-circuit impact current is:

[0017] ;

[0018] Wherein, is the instantaneous current, is the peak voltage on the high-voltage side, is the equivalent short-circuit impedance from the power supply to the fault point, is the initial phase angle when the short circuit occurs, is the time constant, t is the time, is the angular frequency.

[0019] Preferably, the process of capturing the dynamic leakage magnetic field change on the transformer oil tank by the magnetic sensor array includes:

[0020] The magnetic sensor array is parallel to the winding axial direction, symmetrically placed between the core and the winding system, and placed on the transformer oil tank, and used to capture the dynamic leakage magnetic field change on the transformer oil tank.

[0021] Preferably, based on the least square magnetic field inversion algorithm, the magnetic flux density is associated with the deformation amplitude, the winding deformation is diagnosed, and the process of obtaining the diagnosis result includes:

[0022] The least square-based magnetic field inversion algorithm calculates the corresponding deformation rates according to the axial and radial deformation characteristics of the winding, and quantitatively diagnoses the deformation of the winding according to the deformation rates.

[0023] Compared with the prior art, the present application has the following advantages and technical effects:

[0024] The present application proposes a non-invasive real-time diagnostic technology based on leakage magnetic field (LMF) analysis for quantifying winding deformation under SC fault. Unlike traditional methods that rely on gas concentration or frequency-domain parameters, this method uses a high-sensitivity magnetic sensor array to continuously acquire multi-dimensional LMF data, enabling simultaneous assessment of deformation severity and location. The technology combines a least square (LS) algorithm to achieve millisecond-level fault warning, reducing response time (in hours), and reconstructing mechanical stress distribution through magnetic gradient analysis. This method overcomes the temporal and spatial limitations of offline diagnosis, improves measurement safety and diagnostic efficiency, and provides key support for real-time condition monitoring and proactive fault prevention in smart grid systems.

[0025] The present application uses a multi-physics finite element analysis (FEA) model to establish a quantitative relationship between LMF distribution and mechanical deformation, and is verified on a 110kV transformer, thereby advancing transformer diagnosis. A high-sensitivity sensor array is strategically placed on the transformer tank to capture dynamic LMF changes, while a least square (LS)-based inversion algorithm relates magnetic flux density to deformation amplitude. This method uses field-circuit coupled finite element analysis to simulate electromagnetic-mechanical interaction, providing a strong theoretical basis for real-time monitoring thresholds and ensuring compatibility with transformer health assessment frameworks.

[0026] The present application characterizes the vibration conditions of the winding under SC transients, revealing the dynamic mechanical response to electromagnetic forces. It elucidates the LMF distribution patterns related to axial and radial deformation, enabling accurate fault location. A deformation diagnosis system based on LMF characteristic parameters is developed, achieving a diagnostic accuracy of below 1.3% and a response time of milliseconds. These advances provide a novel and economical solution for real-time condition monitoring, supporting predictive maintenance, and improving the reliability of smart grid systems. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of this application, and of the description of the application, are to be interpreted as illustrative only and are not intended to limit the true scope of the application in any way.

[0028] Figure 1 The method flowchart of the embodiments of the present application.

[0029] Figure 2A simulation model diagram of different models of 110kv power transformers of the embodiment of the present application.

[0030] Figure 3 A simulation result diagram of low-voltage winding short-circuit current output of different types of transformers of the embodiment of the present application changing with time.

[0031] Figure 4 A distribution trend diagram of leakage magnetic field in the winding and a corresponding stress component diagram of the embodiment of the present application.

[0032] Figure 5 A transformer side sensor placement area diagram of the embodiment of the present application.

[0033] Figure 6 Axial and radial exploded diagrams of a winding shape deformation model of the embodiment of the present application.

[0034] Figure 7 A diagram of magnetic induction intensity values of different detection points of the winding under different axial deformation degrees of the embodiment of the present application.

[0035] Figure 8 A diagram of magnetic induction intensity values of different detection points of the winding under different axial deformation degrees of the embodiment of the present application.

[0036] Figure 9 A winding deformation judgment process diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0037] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0039] Embodiment one

[0040] As shown in the figure, the winding deformation diagnosis method under transformer short-circuit fault in the embodiment comprises: Figure 1

[0041] Develop a three-dimensional electromagnetic structure finite element model, simulate the interaction of magnetic field and electromagnetic force under short-circuit condition based on the three-dimensional electromagnetic structure finite element model, and establish a nonlinear mapping relationship between the leakage magnetic field distribution and mechanical deformation;

[0042] Capture the dynamic leakage magnetic field change on the transformer oil tank through the magnetic sensor array;​

[0043] The magnetic field inversion algorithm based on least squares is used to associate the magnetic flux density with the deformation amplitude, diagnose the winding deformation, and obtain the diagnosis result.

[0044] Specifically, the winding deformation during the short-circuit fault of the power transformer affects the integrity of the equipment and the reliability of the power grid, and the traditional dissolved gas analysis, frequency response analysis and other diagnostic methods rely on offline procedures and cannot timely discover the fault. The embodiment proposes a non-intrusive real-time diagnostic technology based on leakage magnetic field analysis for quantifying the axial and axial winding deformation. A three-dimensional electromagnetic structure finite element model is developed for a 110kV transformer, a nonlinear mapping relationship between the leakage magnetic field (LMF) distribution and the mechanical stress is established, and the simulation verifies that the deformation increases by 26 times under the fault condition. A high-sensitivity magnetic sensor array is strategically placed on the transformer tank to capture dynamic LMF changes, and an optimized layout principle is followed to improve accuracy. Combined with the magnetic field inversion algorithm based on least squares, the system realizes accurate deformation diagnosis with an error of less than 1.3%, and realizes millisecond-level fault warning. The experiment verifies the accuracy and feasibility of the method, and provides an economical and safe solution for real-time state monitoring of smart grids. The method overcomes the time and space limitations of traditional diagnosis, supports predictive maintenance, and paves the way for advanced fault warning systems.

[0045] Further, the three-dimensional electromagnetic structure finite element model is developed based on a 110kV transformer to simulate the electromagnetic-mechanical interaction of the winding under short-circuit fault conditions.

[0046] Further, the process of establishing a nonlinear mapping relationship between the leakage magnetic field distribution and the mechanical deformation includes:

[0047] The electromagnetic force under short-circuit fault and the short-circuit impulse current are obtained, based on the three-dimensional electromagnetic structure finite element model, combined with the finite element discretization principle, the spatiotemporal distribution of the winding magnetic flux density under the action of transient short-circuit current is obtained, and the dynamic electromagnetic force characteristics are iteratively calculated based on the electromagnetic force formula.

[0048] Specifically, under SC fault, the power transformer experiences strong electromagnetic-mechanical interaction, increasing the risk of winding deformation. Due to the magnetic permeability and geometric structure of the magnetic core material, part of the magnetic field escapes the magnetic core, forming a nonlinear LMF, affecting electromagnetic stability, and exacerbating the fault condition. The embodiment establishes a quantitative mapping relationship between the LMF distribution and the mechanical deformation, and proposes a detection method that converts LMF distortion into a measurable deformation index. This method links electromagnetic anomalies with mechanical conditions, providing a new approach for transformer fault diagnosis.

[0049] Further, the electromagnetic force (F) resulting from the interaction of the current density (I) and the LMF (B) causes a non-uniform stress distribution on the winding conductors, inducing axial elongation and radial contraction; specifically, low-voltage axial elongation, radial contraction, high-voltage axial contraction, and radial expansion.

[0050] electromagnetic force The formula expression is:

[0051] (1);

[0052] In the formula, L is the effective conductor length, I is the current density, and B is the magnetic induction value;

[0053] According to the Biot-Savart law, the magnetic induction value B is expressed by the current I as:

[0054] (2);

[0055] In the formula, μ is the magnetic permeability, dl is the differential current element vector of the source, e r is the unit vector of the current element pointing to the field point, and r is the straight-line distance between the current element and the observation point.

[0056] Further, the formula expression of the short-circuit impulse current is:

[0057] (3);

[0058] wherein, is the instantaneous current, is the peak voltage on the high-voltage side, is the equivalent short-circuit impedance from the power source to the fault point, is the initial phase angle when the short circuit occurs, is the time constant, t is time, is the angular frequency.

[0059] The short-circuit current can be divided into two parts: a transient component (decaying to zero over time) and a steady-state component (varying sinusoidally). Therefore, according to formula (3), when and , both the transient component and the steady-state component reach their maximum values and are in opposite directions. Substituting a 50 Hz transformer, the short-circuit current reaches its maximum value at t = 0.01 s. This short-circuit current can typically reach 10-30 times the normal current.

[0060] Substituting formula (2) into formula (1) gives the expression of the electromagnetic force F removing the magnetic field (formula 4), in which the current term is in the form of a square. Thus, when a transformer suddenly experiences a short-circuit fault, the short-circuit current will suddenly increase, generating a severe impact load in the winding, thereby significantly increasing the risk of mechanical deformation.

[0061] ( );

[0062] can be represented by the voltage U, the short-circuit impedance of the system and the winding

[0063] ( );

[0064] Further, the embodiment establishes a three-dimensional electromagnetic-structure coupling model of 110 kV oil-immersed power transformer, and studies the interaction of magnetic field and electromagnetic force under short circuit (SC) condition (parameters are shown in Table 1). By using the electromagnetic module of finite element simulation software and the mechanical analysis tool of Statics Structure, the model integrates the magnetic core winding geometry, nonlinear material properties and hysteresis effect, and follows the framework of Hyun-Mo Ahn. Based on the finite element discretization principle, the Maxwell equation set (equation 6-equation 9) is solved to obtain the space-time distribution law of winding magnetic flux density under the action of transient short-circuit current, and the dynamic electrodynamic force characteristics are iteratively calculated in combination with the electromagnetic force formula (equation 1). On this basis, the modeling strategy optimizes the setting of winding boundary conditions, significantly improves the operation efficiency while ensuring the accuracy of magnetic field calculation. In view of the electromagnetic-mechanical coupling asymmetry problem caused by winding deformation, a three-dimensional simulation model is specially constructed for targeted analysis. This method effectively balances the calculation complexity and the integrity of the representation of physical phenomena, and provides a reliable numerical analysis tool for the evaluation of the short-circuit resistance of the transformer.

[0065] Table 1 110kv power transformer parameters

[0066]

[0067] ( );

[0068] ( );

[0069] ( );

[0070] ( );

[0071] The embodiment establishes a multi-scale simulation model of a double-winding three-phase transformer and a double-winding single-phase transformer, such as Figure 2 ​The complex model adopts a layered disc winding structure to accurately represent the leakage magnetic field gradient distribution characteristics, while the simple model effectively reduces the complexity of electromagnetic field solution by using a hollow cylinder equivalent winding disc structure. Both models introduce hysteresis characteristics and saturation effects to construct a nonlinear equivalent magnetic circuit, thereby realizing electromagnetic-mechanical cross-scale coupling analysis.

[0072] FEM analysis verifies the accuracy of the theoretical model, and the numerical calculation results are consistent with the first peak value of the short-circuit current t=0.01s derived from formula (3). The distributed air gap design of the multi-stage pie-shaped winding changes the leakage magnetic path structure. Compared with the traditional lumped hollow cylinder model, the equivalent leakage reactance increases, which reduces the amplitude of the low-voltage side short-circuit current, as shown in Figure 3 At the same time, both structures show typical exponential decay oscillation characteristics.

[0073] Field-circuit coupled finite element analysis shows significant magnetic flux density (MFD) gradient, as shown in Figure 4 The high magnetic permeability of the magnetic core layered structure limits most of the magnetic flux, while a small part of the magnetic flux overflows from the iron core structure, showing an exponential axial decay of MFD with distance. In the xz plane, along the 4000mm detection path 300mm away from the winding along the z-axis, the peak MFD near the winding edge is 0.03151T, which decays to below 0.00158T with increasing distance. LMF shows a double-peak distribution, with field strength increasing towards the winding edge and decreasing towards the center, forming a local high-flux area, which is crucial for deformation diagnosis.

[0074] There is a specific current distribution in the winding, as shown in Figure 4 Based on the left-hand rule, the direction of the electromagnetic force acting on it can be determined. For ease of analysis, the electromagnetic force can be decomposed into axial and axial components , and formula (1) is accordingly modified:

[0075] (10);

[0076] (11);

[0077] where, is the component of the leakage magnetic induction value in the x-axis direction; is the component of the leakage magnetic induction value in the z-axis direction;

[0078] The analysis of electromagnetic forces using equations (7) and (8) clearly shows the mechanical behavior during short circuit. The low voltage (LV) winding experiences axial tension and axial centrifugal force, resulting in axial tension and radial expansion. Conversely, the high voltage (HV) winding faces axial compression and axial centripetal force, leading to axial buckling and radial axial contraction. These opposite deformations occur because of the phase difference in winding current during transient energy conversion, which causes the direction of Lorentz force to reverse.

[0079] A multi-physics finite element model based on electromagnetic-mechanical coupling is established to simulate these characteristics for a 110 kV transformer. The Maxwell stress tensor maps the winding current density to the MFD, which is calculated by the finite element simulation software. The resulting force density is applied as a boundary load to the three-dimensional structural model in the multi-physics simulation software, which is discretized, solved for the harmonic response, and the simulation results confirm the axial elongation / compression and axial displacement modes, consistent with the theoretical predictions. Under SC conditions, the maximum displacement increases by 26 times compared to normal operation, significantly increasing the deformation risk. Due to material nonlinearity and residual stress, repeated SC events cause cumulative damage. The cyclic electromagnetic load increases the dislocation density in the copper conductor, while the insulating spacer exhibits nonlinear compression deformation, reducing the axial pre-tension and axial stiffness. The cumulative plastic strain can compromise the stability of the winding, which may lead to complete collapse if not addressed. These findings highlight the need for real-time diagnostics to mitigate irreversible damage in smart grid applications.

[0080] Further, the process of capturing the dynamic leakage magnetic field changes on the transformer tank by the magnetic sensor array includes:

[0081] The magnetic sensor array is parallel to the winding axial direction, symmetrically placed between the core and the winding system, with a placement range approximately equal to the winding length, and installed on the transformer tank to capture the dynamic leakage magnetic field changes on the transformer tank.

[0082] Furthermore, based on LMF attenuation analysis, the sensors are placed close to the winding to maximize detection sensitivity, balancing the feasibility of online monitoring, operational safety, and structural integrity. The array is installed on the side wall of the transformer tank, and multi-sensor data fusion reduces the local LMF distortion caused by single-point measurement. Finite element method (FEM) simulation as shown in Figure 5 reveals a bimodal axial LMF distribution, revealing three layout principles:

[0083] (1) Parallel alignment along the winding axial direction.

[0084] (2) Symmetric placement between the core and the winding system.

[0085] (3) Coverage range similar to the winding length.

[0086] 5 equidistant nodes are distributed along the axial symmetry, the node spacing is 200 mm, and the total coverage area of the node is 1000 mm, which ensures the accuracy of deformation detection and ensures the engineering reliability, as shown in Figure 5 . This configuration supports the diagnostic system by capturing dynamic LMF changes.

[0087] The high-precision finite element model represents the axial and radial winding deformation, as shown in Figure 6 . The axial model quantifies the compression ratio through variable disc segment height, while the axial model captures the inner diameter contraction and outer diameter expansion through radius adjustment. These parameter models standardize the input of mechanical response analysis, providing accurate deformation feature extraction for diagnostic algorithms.

[0088] The LMF affected by multi-physical field coupling is analyzed at 5 axial detection points (points A, B, C, D, and E) outside the winding using the finite element method, as shown in Figure 7 .

[0089] When the winding undergoes axial deformation, the x-axis direction leakage magnetic flux density shows significant variation characteristics with the deformation rate (0.00%-0.08%). Through the observation of the 5 equidistantly arranged detection points, it is found that: except for the center point C, the leakage magnetic induction intensity of the remaining points (points A, B, D, and E) increases monotonously with the increase of deformation rate. Specifically, when the deformation rate increases by 0.01%, the magnetic induction intensity of points A, B, D, and E increases by more than 150 T. In contrast, the change gradient of the center point C is only 29 T, which is significantly lower than that of other points.

[0090] The evolution law of magnetic induction intensity collected by the sensor array is shown in Figure 8 : except for the sensor node C, the leakage magnetic induction intensity of the remaining nodes (points A, B, D, and E) decreases monotonously with the increase of winding deformation rate (0.00%→0.08%). In contrast, the magnetic induction intensity of sensor node C increases. Among them, when the winding deformation rate increases by 0.01%, the leakage magnetic induction intensity of sensor points a and e decreases by 4 T, sensor points b and d decrease by 2 T, but sensor node C only increases by 0.17 T. This characteristic change pattern provides a key diagnostic basis for reconstructing mechanical deformation through magnetic field inversion algorithms.

[0091] Through the analysis of the spatial magnetic field distribution characteristics of the winding under the axial and axial deformation conditions, the sensors (sensor A and sensor E) located at the end of the winding show higher sensitivity to the change of the magnetic flux density compared to other measurement points. Therefore, sensor A or sensor E located at the end of the winding can be selected as the deformation evaluation point for evaluating the severity of winding deformation. In this embodiment, sensor A is designated as the reference benchmark for deformation judgment because it has optimized response characteristics in capturing electromagnetic parameter changes caused by mechanical displacement. The selection principle follows the electromagnetic-mechanical coupling mechanism, that is, the end region shows stronger magnetic field gradient changes when the structure deforms.

[0092] When the winding axial deformation rate is 0.01%, the magnetic flux density increases by 0.17 mT, while the same radial deformation rate only causes a decay of 0.004 mT, with a sensitivity difference of 42.5 times. This feature shows that under the condition of combined deformation, the change of the magnetic flux density of sensor point A mainly reflects the cumulative effect of axial deformation.

[0093] To eliminate the mutual influence of the axial and radial directions, sensor point C is added in this embodiment to improve the accuracy of axial and radial combined deformation judgment through multi-sensor comprehensive judgment. The specific judgment process is as follows:

[0094] The magnetic flux density of sensor point A and sensor point C is monitored, denoted as , . The values of these two points are substituted into the deformation state judgment formula to diagnose the axial deformation and the radial deformation :

[0095] (12);

[0096] where the deformation variable calculation parameter can be obtained as follows: under the premise of safety, the axial or radial deformation of the transformer under a small deformation is obtained in advance, the axial deformation magnetic flux density of points A and C and , and the radial deformation magnetic flux density and . According to the test results, through the least squares method, substitute formula (13), formula (14), formula (15), formula (16) (the relationship between the axial and radial deformation rate and the magnetic flux density of points A and C), and calculate:

[0097] (13);

[0098] (14);

[0099] (15);

[0100] (16);

[0101] where, and are the axial or axial deformation-induced flux densities at sensor location A; and are the axial or axial deformation-induced flux densities at sensor location C, respectively; and are constant coefficients.

[0102] The axial error is generally smaller than the radial error for different winding deformation rates, but the judgment error is less than 1.3%. It is sufficient to accurately identify the axial and radial deformation of the winding. The winding deformation error statistical analysis is shown in Table 2, and the judgment process is shown in Figure 9 .

[0103] Table 2. Statistical analysis of winding deformation error

[0104]

[0105] The embodiment develops a non-invasive real-time diagnosis method for transformer winding deformation under short circuit (SC) fault, which uses leakage magnetic field (LMF) analysis to improve the reliability of smart grid. Through the integration of multi-physical field modeling, optimized sensor array and advanced algorithm, the precise deformation quantization with millisecond response is realized, which overcomes the limitations of traditional offline diagnosis.

[0106] The embodiment establishes a three-dimensional electromagnetic-structure coupling model by the finite element method, and quantifies the nonlinear relationship between superconducting current and mechanical deformation. Under the driving of opposite electromagnetic forces, the low-voltage winding shows axial expansion and axial elongation, while the high-voltage winding shows axial contraction and axial buckling. The optimized magnetic sensor array is arranged along the winding axis and covers its length, capturing dynamic LMF changes. The least squares-based magnetic field inversion algorithm decouples the axial and axial deformations, achieving axial error ≤0.028%, axial error ≤1.21%, and overall error <1.3%. This method surpasses traditional methods such as dissolved gas analysis and frequency response analysis, providing fast fault detection and predictive maintenance capabilities.

[0107] The method of the embodiment provides an economical and safe alternative method for invasive diagnosis, and can achieve early warning in milliseconds through a micro-magnetic sensor. By accurately identifying the phase change pattern, it supports predictive maintenance, improves the reliability of smart grid equipment and reduces the risk of power outages. Its non-invasive design facilitates integration into existing transformer systems and meets the needs of advanced condition monitoring.

[0108] Embodiment two

[0109] The embodiment also discloses a computer device comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of embodiment one.

[0110] Embodiment three

[0111] The embodiment also discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the method of embodiment one.

[0112] Embodiment four

[0113] The embodiment also discloses a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the steps of the method of embodiment one.

[0114] The above is only the preferred specific implementation of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for diagnosing winding deformation under transformer short-circuit faults, characterized in that, include: A three-dimensional electromagnetic structure finite element model was developed and constructed. Based on the three-dimensional electromagnetic structure finite element model, the interaction between magnetic field and electromagnetic force under short-circuit conditions was simulated, and a nonlinear mapping relationship between leakage magnetic field distribution and mechanical deformation was established. Dynamic leakage magnetic field changes on the transformer tank are captured using a magnetic sensor array. Based on the least squares magnetic field inversion algorithm, the magnetic flux density is correlated with the deformation amplitude to diagnose the winding deformation and obtain the diagnostic results; The process of establishing a nonlinear mapping relationship between the leakage magnetic field distribution and mechanical deformation includes: The electromagnetic force and short-circuit impact current under short-circuit fault are obtained. Based on the three-dimensional electromagnetic structure finite element model and combined with the finite element discretization principle, the spatiotemporal distribution law of the winding magnetic flux density under transient short-circuit current is obtained, and the dynamic electrodynamic characteristics are calculated iteratively by combining the electromagnetic force formula. The process of capturing dynamic leakage magnetic field changes on a transformer tank using a magnetic sensor array includes: The magnetic sensor array is aligned parallel to the axial direction of the winding and symmetrically placed between the core and the winding system. The placement range includes the winding length and is installed on the transformer tank to capture the dynamic leakage magnetic field changes on the transformer tank. The least-squares magnetic field inversion algorithm correlates magnetic flux density with deformation amplitude to diagnose winding deformation and obtain diagnostic results. Based on the least squares magnetic field inversion algorithm, the corresponding deformation rate is calculated according to the axial and radial deformation characteristics of the winding, and the deformation of the winding is quantitatively diagnosed based on the deformation rate.

2. The method according to claim 1, characterized in that, The three-dimensional electromagnetic structure finite element model was developed based on a 110kV transformer to simulate the electromagnetic-mechanical interaction of the windings under short-circuit fault conditions.

3. The method according to claim 1, characterized in that, The electromagnetic force The formula expression is: ； In the formula, L is the effective conductor length, I is the current density, and B is the magnetic flux density. According to the Biot-Savart law, the magnetic flux density B is expressed in terms of current I as follows: ； In the formula, μ is the permeability, dl is the differential current element vector of the source, and e r is the unit vector pointing from the current element to the field point, and r is the straight-line distance between the current element and the observation point.

4. The method according to claim 1, characterized in that, The formula for the short-circuit impact current is: ； in, For instantaneous current, This is the peak voltage on the high-voltage side. The equivalent short-circuit impedance from the power source to the fault point. The initial phase angle when the short circuit occurs. Let t be the time constant and t be the time. ω is the angular frequency.

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