Extra-high voltage power transformer winding fault simulation device and diagnosis method based on oscillatory wave detection

Abstract

The invention discloses an extra-high voltage power transformer winding fault simulation device and diagnosis method based on oscillatory wave detection, and the method comprises the steps: firstly testing a test transformer winding, connecting a connection point of the transformer winding with a high-frequency high-voltage switch and a high-frequency high-voltage DC power supply, and enabling the high-frequency high-voltage switch to carry out the periodic operation; therefore, the extra-high-voltage transformer body structure is excited to vibrate; establishing a signal model Xt by adopting ITD transformation according to the test data; the method comprises the following steps of: firstly, acquiring a signal, then decomposing the signal into the sum of a plurality of mutually orthogonal intrinsic mode function (IMF) components by adopting an empirical mode decomposition (EMD) method, and then performing Hilbert transformation on each IMF component to obtain an instantaneous frequency and an instantaneous amplitude, thereby obtaining a Hilbert spectrum of the signal to perform decomposition and reconstruction, improving a signal-to-noise ratio and solving I (X, Y) to judge and remove most of redundant noise and interference signals; and further reconstructing the required signal, and screening the obtained IMF components of each order by using mutual information after the unsteady-state signal is decomposed by EMD to obtain a new x (t). And finally, solving a winding vibration signal after the deformation fault is simulated through a solved signal sequence sample entropy calculation formula, judging whether the deformation fault of the winding is serious according to the winding vibration signal, and identifying the fault degree of the transformer.

Description

Technical Field

[0001] The present invention focuses on the application of oscillating wave detection technology in the field of UHV power transformers, and specifically relates to a simulation device for axial loosening faults of UHV power transformer windings based on oscillating wave detection and a diagnosis method therefor. Background Art

[0002] In the UHV power system, UHV transformers undertake the core mission of high-voltage level power transmission, and the reliability and safety of their operation play a decisive role in the stable operation of the entire power system. However, during the actual operation of UHV transformers, they often encounter frequent impacts of instantaneous loads and strong impacts of accidental short-circuit currents. These impacts are extremely likely to cause the winding clamping parts to fall off, thereby triggering the phenomenon of winding loosening. Once the winding becomes loose, its mechanical bearing capacity will decrease significantly, which will not only interfere with the normal operation of the UHV transformer, but also pose a serious threat to the stable operation of the UHV power transmission and supply system.

[0003] To effectively prevent possible catastrophic consequences, it is of irreplaceable significance to carry out routine inspection work on the transformer windings. In view of this, it is particularly crucial and necessary to accurately simulate the axial loosening fault of the winding with the help of a simulation device for UHV power transformer winding faults based on oscillating wave detection and promptly diagnose the state of the transformer winding. This device and related diagnosis methods can provide strong technical guarantees and support for the stable operation of UHV transformers.

[0004] Since the UHV transformer winding fault simulation device controls the rotation and movement through the rotating screw of the power transformer winding, enabling the high-voltage winding of the power transformer to be translated and rotated arbitrarily in the axial direction to adjust to a suitable position and thus reach the fault simulation position, and then making the winding generate axial loosening at a specified position through the movement of the fault simulation mechanical left arm and the fault simulation mechanical right arm. The vibration signal collected by the high-speed camera on the surface of the transformer oil tank presents the characteristic of a non-constant curve, and there is a close internal relationship between this vibration signal and the state of the winding. Therefore, the state of the winding can be diagnosed based on the change of the output vibration signal.

[0005] However, in view of the fact that there are still many problems to be solved in the prior art in this regard, the present invention innovatively proposes a UHV power transformer winding fault simulation device and diagnostic method based on oscillating wave detection. A mechanical device is specially designed to simulate the situation of the loosening and falling of the winding clamping parts at specific positions and cooperate with a high-speed camera test system. Specifically, with the help of this device and system, the vibration signals of each measuring point on the surface of the UHV transformer oil tank can be calculated, and the vibration signal sample entropy expressions of different regions under different winding states can be deduced, and based on this, the actual state of the winding can be further accurately judged. To sum up, with its unique design and operation mechanism, the present invention can stably, efficiently and reliably achieve the accurate diagnosis of the axial loosening fault of the UHV transformer winding, providing strong technical support and guarantee for the safe and stable operation of the UHV power transformer. Summary of the Invention

[0006] A UHV power transformer winding fault simulation device and diagnostic method based on oscillating wave detection, which uses a mechanical device to simulate the axial looseness of the UHV transformer winding, and combines a high-speed camera to achieve non-contact acquisition of the transformer vibration signal to realize winding diagnosis; including: the high-voltage winding of the power transformer, the power transformer winding support platform, fixed on the power transformer winding support platform, clamping the transformer winding rotating drive gear, the connecting frame fixed on the power transformer winding support platform, the transformer rotating motor fixed on the power transformer winding connecting frame, clamping the transformer winding fixed drive gear, clamping the transformer winding fixed drive knob, located in the axial slider seat, the gear drive motor, the transformer clamping driving gear, the power transformer winding support frame, the robotic arm control motor, the power transformer winding support base, the gear drive motor, the rotating device support frame, the power transformer winding rotating lead screw, the power transformer winding rotating lead screw control frame, the bottom plate seat fixed on the power transformer winding support platform, the support platform front baffle, the power transformer winding rotating sensor, the support platform rear baffle, the power transformer winding clamping sensor, the transformer rotating driven gear, the fault simulation mechanical right arm, the rotating device support platform, the fault simulation mechanical left arm, the support platform front baffle, the power transformer winding rotating gear support platform, the power transformer winding rotating lead screw connecting shaft, the power transformer winding rotating internal gear, the power transformer winding rotating planetary gear, clamping the transformer winding fixed gasket, the power transformer winding rotating internal gear support shaft, the power transformer winding rotating fixing plate, the power transformer winding rotating internal gear rotating column, the fault simulation robotic arm base, the fault simulation robotic arm large arm, the fault simulation robotic arm clamping device, the fault simulation robotic arm clamping device motor, the fault simulation robotic arm small arm, the fault simulation robotic arm waist, the fault simulation robotic arm hand claw, the fault simulation robotic arm hand claw connecting shaft, the fault simulation robotic arm hand claw support platform, the fault simulation robotic arm hand transmission device, the upper bushing of the first high-voltage winding, the upper bushing of the second low-voltage winding, the upper bushing of the third high-voltage winding, the upper bushing of the fourth low-voltage winding, the upper bushing of the fifth high-voltage winding, the upper bushing of the sixth low-voltage winding, the high-frequency high-voltage DC power supply controlled by a high-frequency high-voltage switch, the high-speed camera, the signal acquisition device connected to the high-speed camera, the first high-voltage winding, the second low-voltage winding, the third high-voltage winding, the fourth low-voltage winding, the fifth high-voltage winding and the sixth low-voltage winding; specifically includes the following steps:

[0007] Step 1: Simulate the axial looseness faults at different positions of the windings of the UHV transformer;

[0008] Step 2: Conduct machine vision vibration testing and fault diagnosis on the UHV transformer winding.

[0009] Furthermore, the said Step 1 includes:

[0010] 1) Fix the high-voltage winding of the power transformer by placing it on the center of the rotating lead screw connecting shaft of the power transformer winding through the rotating internal gear support shaft of the power transformer winding and the rotating internal gear of the power transformer winding.

[0011] 2) Rotate the clamping transmission knob for the transformer winding to drive the fixed transmission gear for the transformer winding and the gear drive motor to start working, so that the fixed gasket for the transformer winding on the rotating transmission gear for the clamping transformer winding expands outwards, enabling the spatial position of the high-voltage winding of the power transformer to change.

[0012] 3) Rotate the rotating lead screw of the power transformer winding to drive the rotating lead screw connecting shaft of the transformer winding to rotate, thereby causing the rotating internal gear of the power transformer winding and the rotating planetary gear of the power transformer winding to drive the axial rotation of the rotating internal gear support shaft of the power transformer winding, enabling the high-voltage winding of the power transformer to be translated and rotated arbitrarily in the axial direction and adjusted to the appropriate position.

[0013] 4) The mechanical left arm for fault simulation moves along the connecting frame fixed on the support platform of the power transformer winding through the base of the mechanical arm for fault simulation to the fault simulation position where the winding pressing part is loose. Control the boom, forearm, and clamping device of the mechanical arm for fault simulation to move the gripper of the mechanical arm for fault simulation to the front of the high-voltage winding of the power transformer. The connecting shaft of the gripper of the mechanical arm for fault simulation pushes out the wedge block and inserts it into the middle of the winding discs at both ends of the high-voltage winding of the power transformer to cause looseness. The mechanical arm retracts, and the boom of the mechanical arm for fault simulation resets along the connecting frame fixed on the support platform of the power transformer winding through the base of the mechanical arm for fault simulation.

[0014] 5) The mechanical right arm for fault simulation moves along the connecting frame fixed on the support platform of the power transformer winding through the base of the mechanical arm for fault simulation to the fault simulation position where the winding pressing part is loose. Control the boom, forearm, and clamping device of the mechanical arm for fault simulation to move the gripper of the mechanical arm for fault simulation to the front of the high-voltage winding of the power transformer. Drive the forearm of the mechanical arm for fault simulation to feed and clamp the high-voltage winding of the power transformer. The movement of the mechanical arm completes the extraction of the high-voltage winding of the power transformer. The boom of the mechanical arm for fault simulation resets along the connecting frame fixed on the support platform of the power transformer winding through the moving base of the mechanical arm.

[0015] 6) The malfunction simulation mechanical left arm moves along the connecting frame fixed on the winding support platform of the power transformer through the malfunction simulation robotic arm base to the malfunction simulation position where the winding pressing member is loose. Control the malfunction simulation robotic arm's large arm, small arm, and clamping device to move the malfunction simulation robotic arm's gripper in front of the high-voltage winding of the power transformer. The malfunction simulation robotic arm's gripper retracts the wedge block, the robotic arm retracts, and the malfunction simulation robotic arm's large arm returns to its original position along the connecting frame fixed on the winding support platform of the power transformer through the malfunction simulation robotic arm base;

[0016] 7) Rotate the fixed drive knob for clamping the transformer winding to drive the fixed drive gear for clamping the transformer winding and the gear drive motor to start working, so that the fixed gasket for clamping the transformer winding on the rotating drive gear for clamping the transformer winding contracts inward to control the fixed spatial position of the high-voltage winding of the power transformer to remain stationary.

[0017] 8) Repeat the operations in 2) to 7) to simulate the axial loosening faults of the windings at different positions.

[0018] The second step includes:

[0019] 1) Define the connection point of the upper bushings connecting the first high-voltage winding, the third high-voltage winding, and the fifth high-voltage winding as A; define the connection point of the upper bushings connecting the second low-voltage winding, the fourth low-voltage winding, and the sixth low-voltage winding as B;

[0020] 2) Connect the transformer winding connection points A and B to the high-frequency high-voltage switch and the high-frequency high-voltage DC power supply. The high-frequency high-voltage switch performs periodic actions, thereby exciting the vibration of the UHV transformer body structure.

[0021] When using the traditional frequency method to analyze the vibration test signal components of multiple vibration sources such as the UHV transformer winding, iron core, and cooling system for non-stationary signals, false signals and aliasing problems may occur. Therefore, it is necessary to use the instantaneous frequency to represent the local characteristics of the signal. Thus, the ITD transform is used to establish a signal model:

[0022]

[0023] Among them, L is the baseline extraction operator, Xt is the input signal, Lt = LXt is the baseline signal, Ht = (1 - L)Xt is the intrinsic rotation component, τ k (k = 1, 2, 3, …) is the corresponding moment, HLkXt is the (k + 1)-th layer intrinsic rotation component, and LpXt is the monotonic trend component of the original signal.

[0024] 3) Next, the Empirical Mode Decomposition (EMD) method is used to decompose the signal into the sum of several mutually orthogonal Intrinsic Mode Function (IMF) components. Then, the Hilbert transform is performed on each IMF component to obtain the instantaneous frequency and instantaneous amplitude, thereby obtaining the Hilbert spectrum of the signal for decomposition and reconstruction to improve the signal-to-noise ratio:

[0025]

[0026] Among them, X and Y are different signal representation methods, H(X, Y) is the joint entropy, I(X, Y) is the definition of the mutual information of the signal, p(x(i)) and p(y(j)) are the probability distributions of X and Y, and p(x(i), y(j)) is the joint probability. By calculating I(X, Y), most of the redundant noise and interference signals are judged and removed.

[0027] 4) Further reconstruct the required signal to reduce on-site noise interference: Since the IMF can characterize the dependence relationship between two data, the mutual information can be used to screen the obtained IMF components of each order after the EMD decomposes the non-stationary signal.

[0028]

[0029] Among them, u1(t) and u2(t) are the upper and lower envelope lines connected by local maximum points and local minimum points, m(t) is the average value of the upper and lower envelope lines; h(t) = x(t) - m(t). If h(t) does not meet the IMF conditions, it is regarded as the new x(t). Repeat k times to get h1k(t); SD is the standard deviation to judge whether the screening process terminates. Finally, C1 = h1k(t) and r1(t) = x(t) - C1 are obtained.

[0030] 5) Collect the processed data sets and set them as the original data X i ={x1, x2…, x N}, with a length of N. The embedding dimension m and the similarity capacity r are given in advance. Consider the m-dimensional vector x(i) = [x i , x i+1 ,…x i+m-1 (i = 1, 2,…, N - m). Then, define the distance d[x(i), x(j)] between x(i) and x(j) as the maximum value of the differences between their corresponding elements, that is

[0031] 6) Calculate the distance d[x(i), x(j)] between x(i) and the remaining vectors x(j) (j = 1, 2, ..., N - m, j ≠ i) using the sample entropy method. Count the number of d[x(i), x(j)] less than r and the ratio of this number to the total number of distances N - m - 1, denoted as That is:

[0032]

[0033] 7) Then calculate and obtain The average value of: Then repeat 7) and 8) for the dimension m + 1, that is, for the m + 1 vectors, to obtain Then calculate its average value to obtain B m+1 (r).

[0034] 8) Finally, use the sample entropy calculation formula of this sequence to solve the winding vibration signal after simulating the deformation fault, and use this to judge the severity of the winding deformation fault, as follows:

[0035]

[0036] When N is a finite number, the formula can be expressed as:

[0037]

[0038] 9) The magnitude of the sample entropy is related to the values of m and r. Generally, we take m = 2 and r = 0.2SD, where SD is the standard deviation of the original data.

[0039] If 0.21 ≤ SamEn(m, r, N) < 0.26, it is judged that the winding is in a normal state;

[0040] If 0.26 ≤ SamEn(m, r, N) < 0.31, it is judged that the winding has a slight deformation;

[0041] If 0.31 ≤ SamEn(m, r, N) < 0.36, it is judged that the winding has a serious deformation.

[0042] The present invention focuses on a UHV power transformer winding fault simulation device and diagnostic method based on oscillating wave detection. Through a mechanical device, the occurrence situation of the axial loosening fault of the UHV transformer winding is accurately simulated. On this basis, a high-speed camera is innovatively combined, and with the unique detection system constructed by the oscillating wave detection technology, the goal of non-contact acquisition of the transformer vibration signal is achieved, and then the accurate diagnosis of the winding state is realized, greatly improving the safety and stability level of the UHV power system operation. Description of the Drawings

[0043] Figure 1Schematic diagram of the overall structure of a UHV transformer winding fault simulation device of the present invention;

[0044] Figure 2 Oblique view of a UHV transformer winding fault simulation device of the present invention;

[0045] Figure 3 Top view of a UHV transformer winding fault simulation device of the present invention;

[0046] Figure 4 Rotating partial enlarged view of the high-voltage winding connection of a UHV transformer winding fault simulation device of the present invention;

[0047] Figure 5 Schematic diagram of the structure of the fault diagnosis robotic arm of a UHV transformer winding fault simulation device of the present invention;

[0048] Figure 6 Schematic diagram of the structure of the clamping gripper of the fault diagnosis robotic arm of a UHV transformer winding fault simulation device of the present invention;

[0049] Figure 7 Wiring schematic diagram of the UHV transformer winding;

[0050] Figure 8 Flowchart of the diagnosis method of a UHV transformer winding fault simulation device of the present invention. Specific implementation mode

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0052] As Figure 1, as shown in Figures 2, 3, 4, 5, 6, and 7, a UHV power transformer winding fault simulation device based on oscillating wave detection controls the horizontal translation and rotation of the transformer winding, and drives the selected turn of the winding to undergo extrusion deformation during the translation and rotation to achieve winding diagnosis, including: the high-voltage winding (1) of the power transformer, the power transformer winding support platform (2), fixed on the power transformer winding support platform (3), the clamping transformer winding rotation drive gear (4), the connecting frame (5) fixed on the power transformer winding support platform, the transformer rotation motor (6) fixed on the power transformer winding connecting frame (5), the clamping transformer winding fixed drive gear (7), the clamping transformer winding fixed drive knob (8), located on the axial slider seat (9), the gear drive motor (10), the transformer clamping driving gear (11), the power transformer winding support frame (12), the robotic arm control motor (13), the power transformer winding support base (14), the gear drive motor (15), the rotation device support frame (16), the power transformer winding rotation lead screw (17), the power transformer winding rotation lead screw control frame (18), the bottom plate seat (19) fixed on the power transformer winding support platform, the support platform front baffle (20), the power transformer winding rotation sensor (21), the support platform rear baffle (22), the power transformer winding clamping sensor (23), the transformer rotation driven gear (24), the fault simulation robotic right arm (25), the rotation device support platform (26), the fault simulation robotic left arm (27), the support platform front baffle (28), the power transformer winding rotation gear support platform (29), the power transformer winding rotation lead screw connecting shaft (30), the power transformer winding rotation internal gear (31), the power transformer winding rotation planetary gear (32), the clamping transformer winding fixed gasket (33), the power transformer winding rotation internal gear support shaft (34), the power transformer winding rotation fixing plate (35), the power transformer winding rotation internal gear rotation column (36), the fault simulation robotic arm base (37), the fault simulation robotic arm large arm (38), the fault simulation robotic arm clamping device (39), the fault simulation robotic arm clamping device motor (40), the fault simulation robotic arm small arm (41), the fault simulation robotic arm waist (42), the fault simulation robotic arm gripper (43), the fault simulation robotic arm gripper connecting shaft (44), the fault simulation robotic arm gripper support platform (45), the fault simulation robotic arm hand transmission device (46), the upper bushing (47) of the first high-voltage winding, the upper bushing (48) of the second low-voltage winding, the upper bushing (49) of the third high-voltage winding, the upper bushing (50) of the fourth low-voltage winding, the upper bushing (51) of the fifth high-voltage winding, the upper bushing (52) of the sixth low-voltage winding, the high-frequency high-voltage DC power supply (54) controlled by the high-frequency high-voltage switch (53), the high-speed camera (55), and the signal acquisition device (56) connected to the high-speed camera (55),The first high-voltage winding (57), the second low-voltage winding (58), the third high-voltage winding (59), the fourth low-voltage winding (60), the fifth high-voltage winding (61), and the sixth low-voltage winding (62).

[0053] Figure 8 It is a method for diagnosing winding faults of UHV power transformers based on oscillating wave detection, which is characterized by constructing a signal model by combining ITD, decomposing and reconstructing through empirical mode, and finally using the sample entropy method to solve the vibration signal of the winding after simulating deformation faults, and judging the severity of the winding deformation faults based on this. The specific steps are as follows:

[0054] 1) Define the connection point of the upper bushings connecting the first high-voltage winding (57), the third high-voltage winding (59), and the fifth high-voltage winding (61) as A; define the connection point of the upper bushings connecting the second low-voltage winding (58), the fourth low-voltage winding (60), and the sixth low-voltage winding (62) as B;

[0055] 2) Connect the connection point A and the connection point B of the transformer winding to the high-frequency high-voltage switch (53) and the high-frequency high-voltage DC power supply (54). The high-frequency high-voltage switch (53) performs periodic actions to excite the vibration of the UHV transformer body structure.

[0056] 3) When using the traditional frequency method to analyze the components of the vibration test signals of multiple vibration sources such as the winding, iron core, and cooling system of the UHV transformer for non-stationary signals, false signals and false frequency problems may occur. Therefore, it is necessary to use the instantaneous frequency to represent the local characteristics of the signal. Then, an ITD transform is used to establish a signal model:

[0057]

[0058] Among them, L is the baseline extraction operator, Xt is the input signal, Lt = LXt is the baseline signal, Ht = (1 - L)Xt is the intrinsic rotation component, τ k (k = 1, 2, 3, …) is the corresponding moment, HLkXt is the (k + 1)-th layer intrinsic rotation component, and LpXt is the monotonic trend component of the original signal.

[0059] 4) Next, use the Empirical Mode Decomposition (EMD) method to decompose the signal into the sum of several mutually orthogonal Intrinsic Mode Function (IMF) components, and then perform Hilbert transform on each IMF component to obtain the instantaneous frequency and instantaneous amplitude, so as to obtain the Hilbert spectrum of the signal for decomposition and reconstruction to improve the signal-to-noise ratio:

[0060]

[0061] Among them, X and Y are different representations of signals, H(X, Y) is the joint entropy, I(X, Y) is the definition of the mutual information of signals, p(x(i)) and p(y(j)) are the probability distributions of X and Y, and p(x(i), y(j)) is the joint probability. By calculating I(X, Y), most of the redundant noise and interference signals are judged and removed.

[0062] 5) Further reconstruct the required signals to further reduce on-site noise interference: Since IMF can characterize the dependence relationship between two data, mutual information can be used to screen the obtained IMF components of each order after EMD decomposes the non-steady signal.

[0063]

[0064] Among them, u1(t) and u2(t) are the upper and lower envelope lines connected by local maximum points and local minimum points, m(t) is the average value of the upper and lower envelope lines; h(t) = x(t) - m(t). If h(t) does not meet the IMF conditions, it is regarded as the new x(t). Repeat k times to get h1k(t); SD is the standard deviation to judge whether the screening process terminates. Finally, C1 = h1k(t) and r1(t) = x(t) - C1 are obtained.

[0065] 6) Collect the processed data sets and set them as the original data X i ={x1, x2…, x N}, with a length of N. Predetermine the embedding dimension m and the similarity capacity r. Consider the m-dimensional vector x(i) = [x i , x i+1 , … x i+m-1 (i = 1, 2, …, N - m). Then define the distance d[x(i), x(j)] between x(i) and x(j) as the maximum value of the differences between their corresponding elements, that is:

[0066]

[0067] 7) Calculate the distance d[x(i), x(j)] between x(i) and the remaining vectors x(j) (j = 1, 2,..., N - m, j ≠ i) using the sample entropy method.

[0068] Count the number of d[x(i), x(j)] less than r and the ratio of this number to the total number of distances N - m - 1, denoted as That is:

[0069]

[0070] 8) Then calculate the average value of : Repeat steps 7) and 8) for dimension m + 1, i.e., for m + 1 vectors, to obtain Subsequently, calculate its average value to obtain B m+1 (r).

[0071] 9) Finally, use the sample entropy calculation formula of this sequence to solve the winding vibration signal after simulating the deformation fault, and judge the severity of the winding deformation fault as follows:

[0072]

[0073] When N is a finite number, the formula can be expressed as:

[0074]

[0075] 10) The magnitude of the sample entropy is related to the values of m and r. Generally, we take m = 2 and r = 0.2SD, where SD is the standard deviation of the original data.

[0076] If 0.21 ≤ SamEn(m, r, N) < 0.26, it is judged that the winding is in a normal state;

[0077] If 0.26 ≤ SamEn(m, r, N) < 0.31, it is judged that the winding has undergone slight deformation;

[0078] If 0.31 ≤ SamEn(m, r, N) < 0.36, it is judged that the winding has undergone severe deformation.

Claims

1. A UHV power transformer winding fault simulation device and diagnosis method based on oscillation wave detection, using a mechanical device to simulate the axial looseness of the UHV transformer winding, and combining a high-speed camera to realize non-contact collection of transformer vibration signals to realize winding diagnosis, including: High voltage winding of power transformer, power transformer winding support platform, fixed on power transformer winding support platform, clamping transformer winding rotation transmission gear, connecting frame fixed on power transformer winding support platform, transformer rotating motor fixed on power transformer winding connecting frame, clamping transformer winding fixed transmission gear, clamping transformer winding fixed transmission knob, located on axial slider seat, gear drive motor, transformer clamping active gear, power transformer winding support frame, mechanical arm control motor, power transformer winding support base, gear drive motor, rotating device support frame, power transformer winding rotation screw, power transformer winding rotation screw control frame, bottom plate seat fixed on power transformer winding support platform, support platform front baffle, power transformer winding rotation sensor, support platform rear baffle, power transformer winding clamping sensor, transformer rotation driven gear, fault simulation mechanical right arm, rotating device support platform, fault simulation mechanical left arm, support platform front baffle, power transformer winding rotation gear support platform, power transformer winding rotation screw connecting shaft, power Transformer winding rotating internal gear, power transformer winding rotating planetary gear, clamping transformer winding fixing gasket, power transformer winding rotating internal gear support shaft, power transformer winding rotating fixing plate, power transformer winding rotating internal gear rotating column, fault simulation robot arm base, fault simulation robot arm upper arm, fault simulation robot arm clamping device, fault simulation robot arm clamping device motor, fault simulation robot arm forearm, fault simulation robot arm waist, fault simulation robot arm gripper, fault simulation robot arm gripper connecting shaft, fault simulation robot arm gripper Support platform, fault simulation manipulator hand transmission device, upper end bushing of the first high-voltage winding, upper end bushing of the second low-voltage winding, upper end bushing of the third high-voltage winding, upper end bushing of the fourth low-voltage winding, upper end bushing of the fifth high-voltage winding, upper end bushing of the sixth low-voltage winding, high-frequency high-voltage DC power supply controlled by high-frequency high-voltage switch, high-speed camera, signal acquisition device connected to the high-speed camera, first high-voltage winding, second low-voltage winding, third high-voltage winding, fourth low-voltage winding, fifth high-voltage winding and sixth low-voltage winding; specifically including the following steps: Step 1: Simulate axial looseness faults at different positions of the winding of the UHV transformer; Step 2: Conduct machine vision vibration testing and fault diagnosis of UHV transformer windings. Furthermore, the step 1 comprises: 1) The high-voltage winding of the power transformer is fixed by placing the power transformer winding rotating internal gear support shaft and the power transformer winding rotating internal gear on the center of the power transformer winding rotating lead screw connecting shaft; 2) Turn the transformer winding fixing transmission knob to drive the transformer winding fixing transmission gear and the gear drive motor to start working, so that the transformer winding fixing gasket on the transformer winding rotation transmission gear expands outward, so that the spatial position of the high-voltage winding of the power transformer can be changed. 3) Rotate the transformer winding rotating screw to drive the transformer winding rotating screw connecting shaft to rotate, so that the transformer winding rotating internal gear and the transformer winding rotating planetary gear drive the transformer winding rotating internal gear supporting shaft to rotate axially, so that the high-voltage winding of the transformer can be arbitrarily translated and rotated in the axial direction to adjust to the appropriate position. 4) The left arm of the fault simulation robot moves to the loose fault simulation position of the winding clamp through the fault simulation robot arm base along the connecting frame fixed on the winding support platform of the power transformer, and controls the fault simulation robot arm arm, the fault simulation robot arm arm, and the fault simulation robot arm clamping device to move the fault simulation robot arm claw to the front of the high-voltage winding of the power transformer. The fault simulation robot arm claw connecting shaft pushes out the wedge block and inserts it into the middle of the winding coils at both ends of the high-voltage winding of the power transformer to cause looseness, and the robot arm is retracted, and the fault simulation robot arm arm is reset through the fault simulation robot arm base along the connecting frame fixed on the winding support platform of the power transformer; 5) The right arm of the fault simulation robot moves to the fault simulation position of the loose winding clamp through the fault simulation robot arm base along the connecting frame fixed on the winding support platform of the power transformer, controls the fault simulation robot arm arm, the simulation robot arm arm, and the fault simulation robot arm clamping device to move the fault simulation robot arm claw to the front of the high-voltage winding of the power transformer, drives the fault simulation robot arm arm arm to feed so that it clamps the high-voltage winding of the power transformer, and the robot arm moves to complete the extraction of the high-voltage winding of the power transformer. The fault simulation robot arm arm is reset along the connecting frame fixed on the winding support platform of the power transformer through the robot arm moving base; 6) The left arm of the fault simulation robot moves to the loose winding clamp fault simulation position through the fault simulation robot arm base along the connecting frame fixed on the power transformer winding support platform, and controls the fault simulation robot arm arm, the fault simulation robot arm forearm, and the fault simulation robot arm clamping device to move the fault simulation robot arm claw to the front of the high-voltage winding of the power transformer, the fault simulation robot arm claw retracts the wedge block, the robot arm retracts, and the fault simulation robot arm arm is reset through the fault simulation robot arm base along the connecting frame fixed on the power transformer winding support platform; 7) Turn the transformer winding fixing transmission knob to drive the transformer winding fixing transmission gear and the gear drive motor to start working, so that the transformer winding fixing gasket on the transformer winding rotating transmission gear shrinks inward to control the high-voltage winding fixing space position of the power transformer to remain stationary. 8) Repeat operations 2) to 7) to simulate winding axial looseness faults at different locations. The second step comprises: 1) Define the connection point of the upper end bushings connecting the first high-voltage winding, the third high-voltage winding, and the fifth high-voltage winding as A; define the connection point of the upper end bushings connecting the second low-voltage winding, the fourth low-voltage winding, and the sixth low-voltage winding as B; 2) Connect the transformer winding connection points A and B to the high-frequency, high-voltage switch and the high-frequency, high-voltage DC power supply. The high-frequency, high-voltage switch performs periodic actions to stimulate the vibration of the UHV transformer body structure. When using traditional frequency methods to analyze the components of vibration test signals of multiple vibration sources such as UHV transformer windings, cores and cooling systems, false signals and false frequencies may occur. Therefore, it is necessary to use instantaneous frequency to represent the local characteristics of the signal. Therefore, the ITD transform is used to establish the signal model: in, L is the baseline extraction operator, Xt is the input signal, Lt=LXt is the baseline signal, Ht=(1-L)Xt is the intrinsic rotation component, τ k (k=1,2,3,…) is the corresponding moment, HLkXt is the k+1th layer inherent rotation component, and LpXt is the monotonic trend component of the original signal. 3) In the next step, the Empirical Mode Decomposition (EMD) method is used to decompose the signal into the sum of several mutually orthogonal intrinsic mode function (IMF) components, and then each IMF component is Hilbert transformed to obtain the instantaneous frequency and instantaneous amplitude, thereby obtaining the Hilbert spectrum of the signal for decomposition and reconstruction to improve the signal-to-noise ratio: Where X and Y are different signal representations, H(X,Y) is the joint entropy, I(X,Y) is the mutual information definition of the signal, p(x(i)), p(y(j)) are the probability distributions of X and Y, and p(x(i), y(j)) is the joint probability. 4) Further reconstruct the required signal and reduce the interference of field noise: Since IMF can characterize the dependency between two data, the mutual information can be used to screen the obtained IMF components of each order after the EMD decomposition of the non-stationary signal. Among them, u1(t) and u2(t) are the upper and lower envelopes connecting the local maximum and local minimum points, and m(t) is the average value of the upper and lower envelopes; h(t) = x(t)-m(t). If h(t) does not meet the IMF condition, it is regarded as the new x(t). Repeat k times to get h1k(t); SD is the standard deviation, which determines whether the screening process is terminated. Finally, C1 = h1k(t), r1(t) = x(t)-C1. 5) Collect the processed data and set it as the original data X i ={x1,x2…,x N }, with a length of N, given an embedding dimension m and a similarity capacity r, consider an m-dimensional vector x(i) = [x i ,x i+1 ,…x i+m-1 ](i=1,2,…,Nm), and define the distance d[x(i),x(j)] between x(i) and x(j) as the maximum difference between the corresponding elements of the two, that is, 6) Use the sample entropy method to calculate the distance d[x(i),x(j)] between x(i) and the remaining vectors x(j) (j=1,2,...,Nm,j≠i). Count the number of d[x(i),x(j)] less than r and the ratio of this number to the total number of distances Nm-1, recorded as Right now: 7) Then calculate The average value of: Repeat 7) and 8) for the dimension m+1, that is, for the m+1 vector, and we get B i m+1 (r), and then calculate its average value to get B m+1 (r). 8) Finally, the sample entropy calculation formula of the sequence is used to solve the winding vibration signal after the simulated deformation fault, and the severity of the winding deformation fault is judged based on this, as shown below: When N is a finite number, the formula can be expressed as: 9) The size of the sample entropy is related to the values ​​of m and r. We generally take m=2, r=0.2SD, where SD is the standard deviation of the original data. If 0.21≤SamEn(m,r,N)<0.26, the winding is judged to be in normal state; If 0.26≤SamEn(m,r,N)<0.31, it is judged that the winding has slight deformation; If 0.31≤SamEn(m,r,N)<0.36, it is determined that the winding has been severely deformed.

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