A transformer winding fault simulation device based on a multi-dimensional deformation robot and a decoupling diagnosis method

Abstract

This invention discloses a transformer winding fault simulation device and decoupling diagnosis method based on a multidimensional deformation manipulator, belonging to the field of power equipment condition monitoring technology. The device utilizes servo micro-motion excitation technology, applying static deformation to the winding while simultaneously superimposing dynamic micro-disturbances via a multidimensional manipulator, physically reproducing permanent structural damage and potential mechanical loosening; simultaneously, it constructs inter-turn short circuits using an electrical matrix. Based on this device, this invention proposes an electromechanical parameter decoupling diagnosis algorithm: by acquiring the oscillation wave response of the winding under micro-motion disturbances, it extracts the static corrected Hausdorff distance (MHD) characterizing geometric distortion and the dynamic phase space breathing rate (HDR) characterizing structural stability; it introduces a sensitivity decoupling matrix based on physical truth calibration, inverting the mixed features into the equivalent inductance change ΔL and the equivalent resistance change ΔR. This invention effectively solves the problem of traditional methods' difficulty in distinguishing between mechanical loosening, permanent deformation, and electrical short circuits, achieving accurate quantitative diagnosis of composite faults.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring and fault diagnosis technology, specifically to a transformer winding fault simulation device and diagnosis method that uses a specially designed robotic arm to perform active physical disturbances and decouples electromechanical parameters based on dynamic sensitivity characteristics. Background Technology

[0002] The structural integrity of transformer windings is crucial for the safe operation of power systems. In actual operation, windings often face the challenge of "compound faults": that is, under the impact of short-circuit electrodynamics, the winding may simultaneously experience radial bulging / dentation, axial loosening (mechanical fault), and inter-turn insulation damage (electrical fault). Existing technologies have significant limitations:

[0003] The lack of physical simulation and difficulty in distinguishing states: Most existing experimental devices are single-function, only able to simulate static pressure or short circuits, lacking a device that can accurately reproduce "local radial bulges" and "minor axial loosening" using an external robotic arm without damaging the overall winding structure. In particular, for loosening and permanent deformation, their electrical characteristics are extremely similar in a static state, and traditional devices cannot simulate this dynamic difference.

[0004] Feature aliasing in diagnostic algorithms: Existing frequency response methods (FRA) or oscillatory wave methods mainly rely on correlation comparison of static waveforms. When mechanical deformation and electrical short circuits coexist, their signal characteristics couple, causing local parameter distortions to evolve into a nonlinear superposition of global responses. Traditional algorithms struggle to distinguish whether waveform distortion is caused by changes in inductance (deformation) or resistance (short circuit), and are unable to quantify the specific degree of physical damage.

[0005] Therefore, there is an urgent need in this field for a device and method that introduces an "active dynamic disturbance" mechanism to achieve fundamental decoupling of mechanical and electrical faults by detecting the differences in the sensitivity of the windings to minute displacements.

[0006] Summary of the Invention (I) Technical Problem Solved To address the difficulties of existing technologies in accurately simulating and decoupling complex transformer faults and identifying early loosening hazards, this invention provides a transformer winding fault simulation device and decoupling diagnosis method based on a multi-dimensional deformation manipulator. This invention aims to stimulate the nonlinear response of the faulty winding through "active dynamic disturbance," utilizing the physical essence of "difference in mechanical sensitivity" to achieve accurate differentiation and parametric inversion of mechanical deformation (high sensitivity) and electrical short circuit (low sensitivity).

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] Firstly, a transformer winding fault simulation device with dynamic disturbance excitation function is provided. The device includes a gantry-type main frame, a deformation actuator assembly, and a fault injection matrix board.

[0010] Based on this, a dynamic sensitivity fingerprint database is constructed. The specific process is as follows: The fault simulation device is controlled to perform superposition at a frequency of f under mechanical fault conditions. d The device collects the oscillation wave response signal of the transformer winding under the influence of micro-amplitude periodic disturbances, and calculates the phase space trajectory breathing rate (HDR) using the data processing terminal. At the same time, the device is controlled to superimpose micro-amplitude disturbances of the same frequency under electrical fault conditions, collect the oscillation wave response signal, and calculate the static corrected Hausdorff distance (MHD) using the data processing terminal.

[0011] Subsequently, during the diagnostic phase, the oscillation response signal of the transformer under test is acquired. The data processing terminal is used to extract the principal characteristic modes (EMS) through variational mode decomposition (VMD), and the EMS are reconstructed into a high-dimensional phase space trajectory matrix based on the coordinate delay method. The static geometric features (MHD) and dynamic sensitivity features (HDR) of the trajectory matrix are extracted respectively, and a joint feature vector is constructed. Here, MHD characterizes the overall waveform distortion degree, and HDR characterizes the sensitivity of winding parameters to small displacements.

[0012] An electromechanical coupling impedance decoupling model is established. Using a sensitivity decoupling matrix M pre-calibrated based on a fingerprint database, the eigenvectors are inverted into equivalent inductance change ΔL and equivalent resistance change ΔR. An inductance threshold T is set. L and resistance threshold T R The fault type is determined based on the difference in parameter sensitivity: if ΔL exceeds the limit and ΔR is normal, it is determined to be a single mechanical deformation fault; if ΔR exceeds the limit and ΔL is normal, it is determined to be a single inter-turn short circuit fault; if both exceed the limit, it is determined to be a compound fault.

[0013] On the other hand, the present invention also provides a transformer winding fault simulation device to facilitate the aforementioned data acquisition and testing. This device mainly includes a gantry-type main frame, a winding lifting module, a deformation actuator assembly, and a fault injection matrix board.

[0014] The deformation actuator assembly is equipped with a servo micro-motion excitation mode. Specifically, the deformation actuator assembly includes a vertical lifting slide, a servo electric push rod module, and a bidirectional jaw assembly. The vertical lifting slide drive assembly can move along the vertical axis to accurately locate the fault height; the servo electric push rod module is configured to not only drive the gripper base to feed along the horizontal axis to apply static radial force, but also to perform high-frequency micro-motion displacement to generate dynamic disturbance, thereby applying periodic radial pulsating pressure to the transformer winding through the front jaw assembly; the bidirectional jaw assembly is used to drive the first arc-shaped pressure jaw and the second arc-shaped pressure jaw to open and close in the vertical direction, thereby applying axial compression to the winding.

[0015] Furthermore, the fault injection matrix board integrates several independent switching units. The input terminal of each switching unit is connected to multiple contact probes clamped on different taps of the winding via corresponding flexible connecting wires, and the output terminals of all switching units are interconnected via a short-circuit connecting wire. By controlling the closing of specific switching units, a low-impedance short-circuit path can be constructed between different numbers of turns in the winding using the short-circuit connecting wire.

[0016] (III) Beneficial Effects

[0017] Compared with the prior art, the present invention has the following outstanding substantive features and significant progress:

[0018] 1. Breakthrough "active diagnosis" mechanism: This invention no longer passively receives signals, but actively applies physical excitation (micro-perturbation) through a robotic arm. By utilizing the physical essence of "difference in mechanical sensitivity", it fundamentally solves the problem of overlapping mechanical and electrical fault characteristics.

[0019] 2. Innovative Multidimensional Deformation Robotic Arm Structure: The bidirectional jaw assembly designed in this invention, combined with the servo feed module, constitutes a unique three-degree-of-freedom end effector. It can not only achieve axial compression, but also simulate extremely difficult-to-reproduce radial bulge faults through the "clamping and retraction" motion sequence, and can perform high-frequency micro-motion scanning.

[0020] 3. Physics-driven algorithm closed loop: The core parameters of the algorithm (sensitivity matrix M) are directly derived from the physical experimental data measured by the device, rather than from pure theoretical derivation, which ensures the robustness and accuracy of the diagnostic model under actual working conditions. Attached Figure Description

[0021] Figure 1 This is a top view of the overall structure of the transformer winding fault simulation device in this embodiment of the invention;

[0022] Figure 2 This is a front view of the overall structure of the transformer winding fault simulation device in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the fault injection matrix board in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the deformation actuator assembly in an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram illustrating the connection principle between the transformer winding and the signal detection system in an embodiment of the present invention;

[0026] Figure 6 This is a flowchart of the fault simulation and diagnosis method in an embodiment of the present invention.

[0027] In the diagram, 1. Y-axis centering adjustment slide; 2. Centering drive motor; 3. Top guide rail beam;

[0028] 4. Human-machine interface control terminal; 5. Gantry-type main frame; 6. Windings;

[0029] 7. First vertical guide rail column; 8. Second vertical guide rail column; 9. First contact probe;

[0030] 10. Second contact probe; 11. First flexible connecting wire; 12. Second flexible connecting wire;

[0031] 13. Base platform; 14. Winding positioning chassis; 15. Deformation actuator assembly;

[0032] 16. First signal terminal block; 17. First switching unit; 18. Second switching unit;

[0033] 19. Third switching unit; 20. Short-circuit connecting wire; 21. Fourth switching unit;

[0034] 22. Signal terminal block; 23. Fifth switching unit; 24. Sixth switching unit;

[0035] 25. Seventh switching unit; 26. Eighth switching unit; 27. Eighth signal terminal block;

[0036] 28. Fault injection matrix board; 29. ​​First Y-axis transverse guide rail; 30. Second Y-axis transverse guide rail;

[0037] 31. Physical deformation module frame; 32. Servo electric push rod module; 33. First jaw drive cylinder;

[0038] 34. Second jaw drive cylinder; 35. First arc-shaped pressure jaw; 36. Second arc-shaped pressure jaw;

[0039] 37. First vertical drive cylinder; 38. Second vertical drive cylinder; 39. Clamping base;

[0040] 40. Vertical lifting slide; 41. Horizontal slide rail; 42. Signal excitation source;

[0041] 43. Lead-out wiring at the first end; 44. Lead-out wiring at the last end; 45. Signal acquisition device. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0043] Example 1:

[0044] This invention provides a transformer winding fault simulation and detection system. Please refer to [link / reference]. Figure 1 and Figure 4 Its overall structure includes a gantry-type main frame 5, a deformation actuator assembly 15, a fault injection matrix board 28, and a signal acquisition device.

[0045] The gantry-type main frame 5 consists of a first vertical guide rail column 7, a second vertical guide rail column 8, and a top guide rail beam 3 that spans the tops of both. The bottom is connected by a winding positioning chassis to form a stable and visible test base.

[0046] In this embodiment, the transformer winding 6 to be tested is placed on the base platform 13. The base platform 13 is installed above the winding positioning chassis 14 and is used to adjust the overall height of the winding relative to the frame. To ensure precise alignment between the deformation mechanism and the winding axis, a Y-axis centering adjustment slide 1 is provided on the top of the device. The lateral displacement of the slide is driven by a centering drive motor 2 mounted on the crossbeam. This motor is used to precisely control the Y-axis position of the mechanical structure below, and in conjunction with the top guide beam 3, it can be used to adjust the horizontal centering of the robot arm relative to the winding.

[0047] For further details, please refer to... Figure 2 and Figure 4 The deformation actuator assembly 15 is located on the front side of the transformer winding 6 to simulate radial and axial faults in the winding. The main body of this assembly is a clamp base 39, which can move up and down on a vertical lifting slide 40 to achieve positioning of the winding at different heights. Specifically, the lifting of the clamp base 39 is driven by a first vertical drive cylinder 37 and a second vertical drive cylinder 38. This mechanism ensures the accuracy and stability of the vertical positioning of the slide.

[0048] In this embodiment, the vertical lifting slide 40 is mounted on a horizontal slide rail 41. The reciprocating motion of the gripper base 39 is driven by a servo electric actuator module 32, which contains a high-precision servo motor and a ball screw transmission mechanism, and has both force control and position control modes. It can output large thrust for static destructive simulation, and can also perform high-frequency micro-motion displacement with a frequency of 10Hz-50Hz and an amplitude of ±0.5mm under servo control to generate dynamic disturbances.

[0049] Furthermore, a bidirectional jaw assembly is mounted on the gripper base 39. This assembly includes a first jaw drive cylinder 33 and a second jaw drive cylinder 34, for driving a first arc-shaped pressure jaw 35 and a second arc-shaped pressure jaw 36.

[0050] In this embodiment, the working principle of deformation fault simulation and dynamic disturbance is as follows:

[0051] When the vertical drive mechanism of the device drives the gripper base 39 to the target height, the forward movement of the servo electric push rod module 32 drives the gripper base 39 to move forward, causing the first arc-shaped pressure jaw 35 and the second arc-shaped pressure jaw 36 mounted on it to approach the outer wall of the winding.

[0052] The extension of the servo electric actuator module 32 causes its front-end arc-shaped pressure jaw to generate a radial thrust on the transformer winding 6. Under the action of the thrust, the winding coil is forced to displace along the direction of the force, and this movement is converted into a local depression on the winding surface. Thus, a precise and controllable radial pressure is applied to the outer wall of the winding, realizing the quantitative simulation of radial depression faults.

[0053] Similarly, if the first jaw drive cylinder 33 and the second jaw drive cylinder 34 are controlled to close and clamp the coil cake, and then the servo electric push rod module 32 is controlled to retract backward, a radial tension can be applied to the winding to simulate a radial bulge fault.

[0054] Based on this, in order to detect the mechanical sensitivity of the winding, the servo electric actuator module 32 is controlled to superimpose a sinusoidal micro-motion signal while maintaining static contact. This micro-motion is transmitted to the winding surface through the front pressure jaw. If the winding is loose or deformed, its inductance parameters will oscillate violently with the micro-motion; if it is only an electrical short circuit, the parameters remain basically unchanged.

[0055] In addition, by controlling the first jaw drive cylinder 33 and the second jaw drive cylinder 34 to make the upper and lower pressure jaws move relative to each other or in opposite directions, axial compression force or support force can be directly applied to the coil disc in the clamping area to simulate axial compression or loosening faults.

[0056] For further details, please refer to... Figure 3To enable electrical parameter adjustment during fault simulation, this device also includes a fault injection system. The main body of this system is the fault injection matrix board 28, which integrates the first switch unit 17, the second switch unit 18, the third switch unit 19, the fourth switch unit 21, the fifth switch unit 23, the sixth switch unit 24, the seventh switch unit 25, and the eighth switch unit 26. During testing, the number of faulty turns in the transformer winding 6 is determined by the relative positions of the first contact probe 9 and the second contact probe 10 (e.g., ...). Figure 2 (As shown). The short-circuit contact probe is connected to the first signal terminal 16 and the eighth signal terminal 27 of the switching unit via the first flexible connecting wire 11 and the second flexible connecting wire 12, respectively (as shown). Figure 3 (As shown in the diagram); the output terminals of all switching units are connected via short-circuit connecting wires 20. It should be noted that, for clarity, the figures only schematically depict the first contact probe 9 and the second contact probe 10 and their connecting wires. In actual applications, each switching unit of the matrix board is connected to a corresponding probe to cover multiple target taps of the winding. The fault injection matrix board 28 is equipped with signal terminals 22, which are electrically connected to the I / O output terminals of the human-machine interface control terminal 4 via a multi-core shielded cable. By controlling the closure of specific switching units through the human-machine interface control terminal 4, a low-impedance path can be constructed between different numbers of turns in the winding.

[0057] Furthermore, this device also includes a detection system. This system includes a signal excitation source 42 and a signal acquisition device 45. During testing, the excitation source applies an excitation signal to the transformer winding 6 through the lead-out wire 43 at the first end, and the response signal of the winding is transmitted to the signal acquisition device 45 through the lead-out wire 44 at the second end for recording and analysis.

[0058] In the specific operation process of this invention, the operator can coordinate the operation of each drive motor, cylinder, and detection system through the human-machine interface control terminal 4. First, the centering drive motor 2 is started to align the robot arm with the center of the winding; then, the gripper base 39 is driven to a preset height; next, the servo electric push rod module 32 or jaw drive cylinders 33 and 34 are started to apply a specific type and degree of mechanical deformation; or the micro-motion mode is started for dynamic scanning; or the fault injection matrix board 28 is controlled to apply an electrical short circuit of a specific number of turns; at the same time, the signal acquisition device 45 records the changes in the electrical characteristics of the winding in real time. In this way, the system can establish a direct correspondence between mechanical deformation, electrical faults, and oscillation wave characteristics.

[0059] Example 2:

[0060] This phase aims to construct a dynamic sensitivity fingerprint database containing well-defined physical truth values ​​using the aforementioned device. This fingerprint database independently acquires gradient-based mechanical deformation and electrical short-circuit data using a controlled variable method, and specifically introduces a "dynamic sensitivity" testing step to provide multi-dimensional data support for subsequent algorithms. The specific implementation steps are as follows:

[0061] Health baseline acquisition: With the transformer winding in a fault-free state, the servo electric push rod module is controlled to acquire oscillation wave signals through a signal acquisition device in both static and micro-motion states, which serve as reference waveform data.

[0062] Mechanical fault sample generation; with the electrical connection disconnected, the deformation actuator assembly sequentially applies gradient radial displacement (e.g., 1mm-10mm) and different degrees of axial compressive force to the winding. At each fault setting point, the servo electric push rod module performs an additional "micro-scan" to collect dynamic response signals and mark the corresponding true value of physical deformation.

[0063] Electrical fault samples are generated; the mechanical structure is reset, and different combinations of switching units are sequentially closed using a fault injection matrix board to simulate gradient inter-turn short-circuit faults (e.g., 1-10 turns). A "micro-scan" is also performed to acquire signals and mark the true value of the number of short-circuit turns.

[0064] Fingerprint database synthesis; data is compiled into a dynamic sensitivity fingerprint database containing dynamic sensitivity features. Example 3:

[0065] The diagnostic method in this embodiment relies on the aforementioned hardware system, with the processor and memory built into the human-computer interaction control terminal working together to execute the procedure. The memory stores a computer program and a pre-built dynamic sensitivity fingerprint database.

[0066] During actual diagnosis, the processor reads instructions from memory and executes the following specific processing steps:

[0067] Step 1: Signal Acquisition and Preprocessing

[0068] First, the processor receives the original digital signal f(t) of the transformer winding oscillation wave transmitted by the signal acquisition device through the communication interface. Since the field signal contains noise, the processor calls its internal Variational Mode Decomposition (VMD) module to extract the effective components. The core of VMD is to solve the following constrained variational problem, decomposing the signal into K intrinsic mode functions u. k (t):

[0069]

[0070] Among them, {u k} represents the modal components obtained from the decomposition, {ωk} represents the center frequency corresponding to each mode.

[0071] The processor calculates the energy value of each modal component, automatically selects the component with the largest energy proportion as the main feature signal u(t), performs Z-score normalization on it, and temporarily stores the processed data in random access memory (RAM).

[0072] Step 2: Phase Space Reconstruction Operation

[0073] The processor reads the temporarily stored u(t), and based on the Takens embedding theorem, uses the coordinate delay method to map the one-dimensional time series into a high-dimensional data structure, constructing the trajectory matrix X in memory:

[0074]

[0075] Each phase point vector x i Defined as:

[0076]

[0077] In the formula, N = L − (m − 1)τ is the total number of phase points, and L is the signal length. The delay time τ is automatically determined by the processor by calculating the minimum value of the mutual information function; the embedding dimension m is determined by the processor using the spurious nearest neighbor algorithm.

[0078] Step 3: Calculation of dynamic and static two-dimensional features

[0079] At this stage, the processor performs feature extraction operations on both static and dynamically perturbed data:

[0080] (1) Static Feature Calculation (MHD):

[0081] The processor calls the distance calculation subroutine to calculate the distance between the trajectory matrix X to be measured and the pre-stored health baseline trajectory X in memory. ref The corrected Hausdorff distance between them:

[0082] First, define the shortest distance from point a to set B:

[0083]

[0084] Traditional Hausdorff distance calculations, which take the maximum value, are easily affected by noise points. This invention uses an average value correction method.

[0085]

[0086]

[0087] The final bidirectional corrected Hausdorff distance is defined as:

[0088]

[0089] The calculation result H mod It is marked as a characteristic quantity that characterizes the overall distortion degree of the static waveform of the winding.

[0090] (2) Dynamic Feature Calculation (HDR):

[0091] The processor retrieves two sets of signal data collected by the device before and after the micro-disturbance, and reconstructs them into trajectory X. static and X dynamic And call the geometric volume calculation algorithm to calculate the hypervolume difference rate between the two:

[0092]

[0093] This feature is the core innovative indicator of this invention, used to quantify the instability of the winding mechanical structure under micro-disturbances.

[0094] If the winding has mechanical loosening or non-permanent deformation, its equivalent inductance parameter L will change with a small displacement A. x Severe fluctuations cause significant "expansion" or "contraction" of the phase space trajectory, resulting in a significant increase in the HDR value; conversely, for electrical short circuits or healthy windings, the structural stiffness is high, the parameters are not sensitive to micro-motion, and the HDR value approaches zero.

[0095] Step 4: Parameter Inversion and Fault Quantization

[0096] The processor retrieves the pre-calibrated sensitivity decoupling matrix M from memory (this matrix is ​​generated using the least squares method based on the orthogonal physical experimental data described in Example 2) and performs the following linear transformation operation:

[0097]

[0098] Through this operation, the processor outputs two physical quantification metrics: the change in equivalent inductance ΔL and the change in equivalent resistance ΔR.

[0099] Step 5: Automatic Diagnosis and Result Output

[0100] The processor compares the calculated ΔL and ΔR with the preset threshold (T) in memory. L T R Perform logical comparison and generate control instructions according to the following rules:

[0101] If ΔL <T L And ΔR <T R The processor generates a device normal report;

[0102] If ΔL≥T L And ΔR <T RThe processor generates a "single mechanical deformation" diagnostic report and displays the estimated deformation amount on the screen of the human-machine interaction control terminal.

[0103] If ΔL <T L And ΔR≥T R The processor generates a "single-turn short circuit" diagnostic report and the number of short-circuited turns;

[0104] If ΔL≥T L And ΔR≥T R The processor triggers a "composite fault" audible and visual alarm signal.

[0105] Furthermore, it should be emphasized that the specific mechanical structures, driving components, parameter ranges, and basic algorithms described in the embodiments of this invention are merely preferred embodiments, intended to help those skilled in the art understand the core physical driving diagnostic mechanism of this invention, and not an absolute limitation on the scope of protection of this invention. In practical industrial applications, those skilled in the art, based on their understanding of the principles of this invention, can fully utilize well-known methods in the field to make equivalent substitutions or modifications to specific technical points. For example:

[0106] (1) Equivalent replacement of driving and execution components: The “first jaw driving cylinder” and “second jaw driving cylinder” involved in the embodiment are not limited to pneumatic drive, and can also be replaced by hydraulic cylinder, micro linear motor or electric push rod; the “servo electric push rod module” can also adopt other servo drive mechanisms with high frequency micro displacement capability such as piezoelectric ceramic actuator, hydraulic servo system, etc.

[0107] (2) Adaptive adjustment of parameter range: The radial displacement gradient “1mm-10mm” and micro-motion frequency “10Hz-50Hz” mentioned in the embodiment are only demonstration values ​​set for a specific type of transformer. In actual application, the applied displacement range and micro-motion scanning frequency band can be adaptively adjusted according to the capacity level, coil size and material stiffness of the transformer under test.

[0108] (3) Equivalent replacement of mathematical algorithms: The “variational mode decomposition (VMD)” used to extract principal components in the signal preprocessing stage can also be replaced by equivalent non-stationary signal decomposition algorithms such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), wavelet packet transform or empirical wavelet transform (EWT); the volume calculation method used in feature extraction is not limited to the convex hull algorithm.

[0109] Any conventional substitutions, combinations, or modifications made to the specific physical structure, experimental parameter range, and basic mathematical model mentioned above without departing from the core design concept of this invention shall be covered within the protection scope of this invention.

[0110] 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 process, method, article, or apparatus.

[0111] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for decoupling and diagnosing transformer winding faults based on a multidimensional deformation manipulator, characterized in that, Includes the following steps: S1: Signal Acquisition and Preprocessing: Acquire the oscillation response signal of the transformer under test, extract the principal characteristic modes through variational mode decomposition (VMD) using a processor, and perform normalization processing; S2: Phase space reconstruction: Using the processor based on the coordinate delay method, the principal characteristic modes are reconstructed into a high-dimensional phase space trajectory matrix X; S3: Feature extraction: Calculate the static geometric feature of the trajectory matrix—Modified Hausdorff distance (MHD)—and the dynamic sensitivity feature—phase space trajectory breathing rate (HDR)—to construct a joint feature vector V; S4: Parameter inversion decoupling: Call the preset sensitivity decoupling matrix M to map the joint feature vector V into the equivalent inductance change ΔL and the equivalent resistance change ΔR; wherein, the sensitivity decoupling matrix M is generated in advance by using a fault simulation device with dynamic disturbance excitation function on fault samples with known physical truth values. S5: Fault determination: Based on the preset inductance threshold T L and resistance threshold T R Make a judgment: If ΔL <T L And ΔR <T R This is considered a normal state. If ΔL≥T L And ΔR <T R It was determined to be a single mechanical deformation fault; If ΔL <T L And ΔR≥T R It was determined to be a single inter-turn short circuit fault; If ΔL≥T L And ΔR≥T R It was determined to be a complex fault.

2. The method according to claim 1, characterized in that: The trajectory matrix X mentioned in step S2 is defined as follows: Wherein, each phase point vector x i Defined as: in, τ is the signal sequence after standardization of the main characteristic modes, m is the embedding dimension, and N is the total number of phase points.

3. The method according to claim 1, characterized in that: The method for calculating the phase space trajectory breathing rate (HDR) in step S3 is as follows: calculate the static trajectory X before and after applying the micro-motion perturbation. static With dynamic trajectory X dynamic rate of hypervolume change in phase space: Where Vol(⋅) represents the convex hull volume of the phase space attractor, and A x The amplitude of the physical displacement caused by the micro-motion disturbance applied to the surface of the winding.

4. The method according to claim 1, characterized in that: The calibration process of the sensitivity decoupling matrix M in step S4 is as follows: control the fault simulation device to superimpose micro-amplitude disturbances under mechanical fault states and electrical fault states with known physical truth values, collect signals and calculate the corresponding HDR and MHD, and use the least squares method to solve the objective function J=∑║M·VF true ║ 2 We obtain , where is the theoretical impedance change vector corresponding to the physical true value.

5. A transformer winding fault diagnosis system, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the steps of the method as described in any one of claims 1 to 4.

6. A device for simulating transformer winding faults, characterized in that, include: The system comprises a gantry-type main frame, a winding lifting module, a deformation actuator assembly, and a fault injection matrix board. The deformation actuator assembly, suspended on the frame, includes a vertical lifting slide, a servo electric push rod module, and a bidirectional jaw assembly. The servo electric push rod module includes a servo motor and a ball screw transmission mechanism driven by the motor, configured to drive the gripper base to feed along a horizontal axis to apply radial force to the transformer winding placed therein, and superimposed with periodic high-frequency micro-motion displacement. The bidirectional jaw assembly includes a first jaw drive cylinder and a second jaw drive cylinder, which drive the first arc-shaped pressure jaw and the second arc-shaped pressure jaw to open and close relative to each other to apply axial pressure to the transformer winding.

7. The apparatus according to claim 6, characterized in that: The fault injection matrix board integrates several independent switching units. The input terminal of each switching unit is connected to a contact probe used to clamp onto the transformer winding tap, and the output terminals of all switching units are interconnected through short-circuit connection wires.

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