Power transformer fault detection method and device, storage medium and computer equipment

Abstract

The invention discloses a power transformer fault detection method and device, a storage medium and computer equipment, and the method comprises the steps: collecting the magnetic flux density component value of each wire cake in a target collection axial direction according to a preset time interval for each wire cake in a target observation path in a preset voltage period corresponding to a target power transformer, the magnetic flux density component values of the wire cakes collected at the same preset time interval are stored in the same magnetic flux density component value group according to the arrangement sequence of the wire cakes; determining a magnetic flux density component peak point value of the target observation path and a target magnetic flux density component value group where the magnetic flux density component peak point value is located based on the magnetic flux density component value group of a plurality of preset time intervals in a preset voltage period; and based on the storage position of the magnetic flux density component peak point value in the target magnetic flux density component value group, in the line cake arrangement sequence of the target observation path, determining a target line cake with a short circuit fault. The fault wire cake position can be determined, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment diagnosis, and in particular to a method and device for detecting power transformer faults, a storage medium, and a computer device. Background Art

[0002] Power transformers are vital equipment in the power system. With the improvement of living standards, people have higher requirements for power demand and power reliability. If a transformer fails, it may cause a large-scale power outage in the power system, which will have a serious impact on people's production and life. At present, when a transformer fails, especially a short-circuit fault, if the fault is not handled in time, it will cause a serious accident. Summary of the invention

[0003] In view of this, the present application provides a method and device for detecting faults of a power transformer, a storage medium, and a computer device. Within a preset voltage cycle corresponding to a target power transformer, for each coil in a target observation path, the magnetic flux density component value of each coil in the target collection axis is collected according to a preset time interval, wherein the magnetic flux density component values ​​of each coil collected at the same preset time interval are stored in the same magnetic flux density component value group according to the coil arrangement order; based on the magnetic flux density component value groups of multiple preset time intervals within the preset voltage cycle, the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located are determined; based on the storage position of the magnetic flux density component peak value in the target magnetic flux density component value group, the target coil with a short circuit fault is determined in the coil arrangement order of the target observation path. The fault coil position can be specifically located to improve maintenance efficiency.

[0004] According to one aspect of the present application, a method for detecting a power transformer fault is provided, the method comprising:

[0005] Based on the type of the target power transformer with a short-circuit fault, determine the target observation path and the target collection axis of the magnetic flux density component of the target power transformer, wherein different types of power transformers correspond to preset observation paths and preset collection axes of the magnetic flux density component, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is the x-axis, the y-axis or the z-axis;

[0006] Within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target acquisition axis is collected at a preset time interval, wherein the magnetic flux density component values ​​of each coil collected at the same preset time interval are stored in the same magnetic flux density component value group according to the coil arrangement order;

[0007] Based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle, determining the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located;

[0008] Based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group, a target coil having a short circuit fault is determined in the coil arrangement sequence of the target observation path.

[0009] Optionally, the determining of the peak value of the magnetic flux density component of the target observation path based on a group of magnetic flux density component values ​​at multiple preset time intervals within a preset voltage cycle includes:

[0010] Based on the total number of line cakes and the sliding window scale determination formula, multiple sliding window scales are determined;

[0011] For any magnetic flux density component value group within a preset voltage cycle, obtaining a magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales;

[0012] Based on the magnetic flux density component scale analysis value group, the candidate magnetic flux density component peak value of the magnetic flux density component value group is determined, and based on the candidate magnetic flux density component peak value of each magnetic flux density component value group, the magnetic flux density component peak value of the target observation path is determined, wherein the sliding window scale determination formula is:

[0013] L=[N / 2]-1,k∈[1,2,3,...L],

[0014] k represents the sliding window size, which is a natural number greater than or equal to 1 and less than or equal to the third process parameter L, [N / 2] represents the smallest integer less than or equal to N / 2, and N represents the total number of line cakes.

[0015] Optionally, obtaining a magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales includes:

[0016] Select any sliding window scale, and determine the magnetic flux density component scale analysis value group of the magnetic flux density component value group under the selected sliding window scale according to the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, wherein the magnetic flux density component scale analysis value determination formula is:

[0017]

[0018] a k,i Indicates the i-th magnetic flux density component value S in the magnetic flux density component value group under the sliding window scale k iCorresponding magnetic flux density component scale analysis values.

[0019] Optionally, determining the candidate magnetic flux density component peak point values ​​of the magnetic flux density component value group based on the magnetic flux density component scale analysis value group includes:

[0020] Based on the magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales, constructing a magnetic flux density component scale analysis value matrix;

[0021] In the flux density component scale analysis value matrix, the target storage position of the candidate flux density component peak point value in the flux density component value group is determined based on the matrix column where the value 1 appears the most times, and the candidate flux density component peak point value is determined in the flux density component value group based on the target storage position.

[0022] Optionally, determining the peak value of the magnetic flux density component of the target observation path based on the candidate magnetic flux density component peak value of each magnetic flux density component value group includes:

[0023] For each candidate magnetic flux density component peak value of each magnetic flux density component value group, the minimum value among the absolute values ​​of the candidate magnetic flux density component peak value is determined as the magnetic flux density component peak value of the target observation path.

[0024] Optionally, the coil includes a plurality of turns of coils, and after determining a target coil having a short circuit fault in a coil arrangement sequence of a target observation path based on a storage position of the magnetic flux density component peak value in a target magnetic flux density component value group, the method further includes:

[0025] Based on the peak value of the magnetic flux density component and the peak width calculation formula, the peak width is calculated, and based on the peak width, the number of turns with short circuit faults in the target coil is determined, wherein the peak width calculation formula is:

[0026]

[0027] W represents the peak width, x1 and x2 represent the first process parameter and the second process parameter respectively, S max Indicates the peak value of the magnetic flux density component, S p and S p+1 Respectively represent the target magnetic flux density component value group that satisfies The pth and p+1th target magnetic flux density component values, S q and S q+1 Respectively represent the target magnetic flux density component value group that satisfies The qth and q+1th target magnetic flux density component values, x p and xp+1 Respectively represent the position number values ​​of the pth and p+1th target magnetic flux density component values ​​in the target magnetic flux density component value group, x q and x q+1 They respectively represent the position sequence values ​​of the qth and q+1th target magnetic flux density component values ​​in the target magnetic flux density component value group.

[0028] Optionally, determining the number of turns in the target coil having short circuit faults based on the peak width includes:

[0029] Among multiple preset peak width range intervals corresponding to the target power transformer, a target peak width range interval including the peak width is determined, and based on the preset number of turns corresponding to the target peak width range interval, the number of turns in which short circuit faults occur in the target coil is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns in which short circuit faults occur.

[0030] According to another aspect of the present application, a power transformer fault detection device is provided, the device comprising:

[0031] An observation path determination module is used to determine a target observation path and a target collection axis of a magnetic flux density component of the target power transformer based on the type of the target power transformer with a short circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes of magnetic flux density components, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is an x-axis, a y-axis or a z-axis;

[0032] A magnetic flux density component acquisition module is used to acquire the magnetic flux density component value of each wire cake in the target acquisition axis according to a preset time interval within a preset voltage cycle corresponding to the target power transformer, for each wire cake in the target observation path, wherein the magnetic flux density component value of each wire cake acquired at the same preset time interval is stored in the same magnetic flux density component value group according to the arrangement order of the wire cakes;

[0033] A magnetic flux density component peak value determination module, used to determine the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle;

[0034] The short circuit fault detection module is used to determine the target coil having a short circuit fault in the coil arrangement sequence of the target observation path based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group.

[0035] Optionally, the magnetic flux density component peak value determination module is further used to:

[0036] Based on the total number of line cakes and the sliding window scale determination formula, multiple sliding window scales are determined;

[0037] For any magnetic flux density component value group within a preset voltage cycle, obtaining a magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales;

[0038] Based on the magnetic flux density component scale analysis value group, the candidate magnetic flux density component peak value of the magnetic flux density component value group is determined, and based on the candidate magnetic flux density component peak value of each magnetic flux density component value group, the magnetic flux density component peak value of the target observation path is determined, wherein the sliding window scale determination formula is:

[0039] L=[N / 2]-1,k∈[1,2,3,...L],

[0040] k represents the sliding window size, which is a natural number greater than or equal to 1 and less than or equal to the third process parameter L, [N / 2] represents the smallest integer less than or equal to N / 2, and N represents the total number of line cakes.

[0041] Optionally, the magnetic flux density component peak value determination module is further used to:

[0042] Select any sliding window scale, and determine the magnetic flux density component scale analysis value group of the magnetic flux density component value group under the selected sliding window scale according to the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, wherein the magnetic flux density component scale analysis value determination formula is:

[0043]

[0044] a k,i Indicates the i-th magnetic flux density component value S in the magnetic flux density component value group under the sliding window scale k i Corresponding magnetic flux density component scale analysis values.

[0045] Optionally, the magnetic flux density component peak value determination module is further used to:

[0046] Based on the magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales, constructing a magnetic flux density component scale analysis value matrix;

[0047] In the flux density component scale analysis value matrix, the target storage position of the candidate flux density component peak point value in the flux density component value group is determined based on the matrix column where the value 1 appears the most times, and the candidate flux density component peak point value is determined in the flux density component value group based on the target storage position.

[0048] Optionally, the magnetic flux density component peak value determination module is further used to:

[0049] For each candidate magnetic flux density component peak value of each magnetic flux density component value group, the minimum value among the absolute values ​​of the candidate magnetic flux density component peak value is determined as the magnetic flux density component peak value of the target observation path.

[0050] Optionally, the coil comprises a plurality of turns of coil, and the short circuit fault detection module is further used for:

[0051] Based on the peak value of the magnetic flux density component and the peak width calculation formula, the peak width is calculated, and based on the peak width, the number of turns with short circuit faults in the target coil is determined, wherein the peak width calculation formula is:

[0052]

[0053] W represents the peak width, x1 and x2 represent the first process parameter and the second process parameter respectively, S max Indicates the peak value of the magnetic flux density component, S p and S p+1 Respectively represent the target magnetic flux density component value group that satisfies The pth and p+1th target magnetic flux density component values, S q and S q+1 Respectively represent the target magnetic flux density component value group that satisfies The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position number values ​​of the pth and p+1th target magnetic flux density component values ​​in the target magnetic flux density component value group, x q and x q+1 They respectively represent the position sequence values ​​of the qth and q+1th target magnetic flux density component values ​​in the target magnetic flux density component value group.

[0054] Optionally, the short circuit fault detection module is further used to:

[0055] Among multiple preset peak width range intervals corresponding to the target power transformer, a target peak width range interval including the peak width is determined, and based on the preset number of turns corresponding to the target peak width range interval, the number of turns in which short circuit faults occur in the target coil is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns in which short circuit faults occur.

[0056] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned power transformer fault detection method is implemented.

[0057] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned power transformer fault detection method when executing the program.

[0058] By means of the above technical scheme, the present application provides a method and device for detecting faults of a power transformer, a storage medium, and a computer device. Within the preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target collection axis is collected according to the preset time interval, wherein the magnetic flux density component value of each coil collected at the same preset time interval is stored in the same magnetic flux density component value group according to the coil arrangement order; based on the magnetic flux density component value groups of multiple preset time intervals within the preset voltage cycle, the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located are determined; based on the storage position of the magnetic flux density component peak value in the target magnetic flux density component value group, the target coil with a short circuit fault is determined in the coil arrangement order of the target observation path. The fault coil position can be specifically located to improve maintenance efficiency.

[0059] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0061] Figure 1 A schematic diagram of a flow chart of a power transformer fault detection method provided in an embodiment of the present application is shown;

[0062] Figure 2 A schematic diagram of an observation path provided in an embodiment of the present application is shown;

[0063] Figure 3 A schematic diagram of the association relationship between an observation path and a wire cake provided in an embodiment of the present application is shown;

[0064] Figure 4 A schematic diagram of a flow chart of another power transformer fault detection method provided in an embodiment of the present application is shown;

[0065] Figure 5 A schematic diagram of a flow chart of another power transformer fault detection method provided in an embodiment of the present application is shown;

[0066] Figure 6 A schematic structural diagram of a power transformer fault detection device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0067] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0068] In this embodiment, a method for detecting a power transformer fault is provided. Figure 1 As shown, the method includes:

[0069] Step 101, based on the type of the target power transformer that has a short circuit fault, determine the target observation path of the target power transformer and the target collection axis of the magnetic flux density component, wherein different types of power transformers correspond to preset observation paths and preset collection axes of the magnetic flux density component, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is the x-axis, the y-axis or the z-axis.

[0070] Traditional transformer protection measures mainly include current differential protection, dissolved gas in oil method, partial discharge measurement, etc. The dissolved gas in oil method collects the content of dissolved gas in oil and analyzes the oil chromatographic abnormalities, but there are many reasons for oil chromatographic abnormalities. The changes caused by minor faults are small and cannot be detected. The same is true for current differential protection. Therefore, these measures cannot quickly and sensitively judge transformer inter-turn short-circuit faults. Current methods for transformer fault diagnosis include transformer vibration characteristic analysis and frequency response characteristic analysis based on the Internet of Things, but they are greatly affected by measurement conditions and external interference.

[0071] When the transformer is working, the energy is transmitted through the alternating magnetic field of the winding. Therefore, when the winding fails, the leakage magnetic field changes, and a small proportion of short circuit still changes significantly in the transformer leakage magnetic field characteristics. The transformer leakage magnetic data (magnetic flux density component) can reflect the state of the transformer winding. Therefore, by studying the leakage magnetic distribution characteristics of the transformer winding, the early faults of the transformer winding are studied, which not only makes up for the shortcomings of the traditional method of insufficient speed and sensitivity, but also enriches the method of transformer fault diagnosis. According to historical data, winding faults occur most frequently among transformer faults. Among the winding faults, turn-to-turn short-circuit faults occur the most frequently, and the degree is gradually expanding; therefore, the earlier the early turn-to-turn short-circuit faults are detected and measures are taken in advance, the more effectively the losses caused by the faults can be reduced.

[0072] In the above embodiment of the present application, based on the type of target power transformer with a short-circuit fault, the target observation path of the target power transformer and the target collection axis of the magnetic flux density component are determined. Different types of power transformers correspond to preset observation paths and preset collection axes of the magnetic flux density component. The preset observation path is located inside the power transformer, and the preset collection axis is the x-axis, y-axis or z-axis. Taking an oil-immersed transformer with a capacity of 250kVA and a voltage of 10 / 0.4kV as an example, the observation path is, for example, Figure 2 As shown, Figure 2 There are three initial observation paths in total. The final preset observation path is determined among the three initial observation paths. There are multiple line cakes arranged on the preset observation path, such as Figure 3 As shown, Figure 3 There are three initial observation paths, L1, L2, and L3, with a path height of 800mm, a coil height of 300mm, 15 inner windings, and a coil height of 20mm per coil. The coils are numbered 1 to 15 from bottom to top. Regarding the selection of the preset observation path, the normal working condition of the transformer can be simulated and calculated, and the flux density modulus of the three observation paths can be plotted. The path with the largest flux density modulus value among the three observation paths is used as the preset observation path. The flux density modulus is synthesized by the three vector fields of x, y, and z. The magnetic sensor also measures the magnetic vector. Therefore, it is more realistic to analyze different vectors of the magnetic field. Selecting the axial direction with the largest component value can reflect obvious fault changes. For example, the target acquisition axial direction of the flux density component of the aforementioned oil-immersed transformer can be set to the z-axis. In particular, the z-axis is the axial direction, the x-axis is the radial direction, and the y-axis is the tangential direction.

[0073] Step 102, within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target collection axis is collected at a preset time interval, wherein the magnetic flux density component values ​​of each coil collected at the same preset time interval are stored in the same magnetic flux density component value group according to the coil arrangement order.

[0074] The magnetic field also changes periodically during the operation of the transformer, and the change cycle is synchronized with the voltage. Through the analysis of the transformer leakage magnetic curve, it is found that in the short-circuit state, the curve change value at the fault position is always larger than the normal state (the rising value is large when rising, forming a convexity, and the decreasing value is large when decreasing, forming a concave). It is found that there are always special moments in the waveform during a cycle of inter-turn short circuit in the transformer. To this end, within the preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component corresponding to each coil is collected according to the preset time interval, and the target collects the magnetic flux density component value in the axial direction, wherein the magnetic flux density component values ​​of each coil collected at the same preset time interval are stored in the same magnetic flux density component value group according to the coil arrangement order. Specifically, under the premise of selecting L1 as the target observation path, the magnetic flux density z component data of each of the 15 measurement points (i.e., 15 coils) on the observation path can be collected by the Hall sensor. The sampling frequency is 1000 Hz, and the frequency of leakage magnetic change is the same as the alternating current frequency, which is 50 Hz, that is, the change cycle is 0.02 seconds, and 20 samples can be sampled in one cycle. The proposed leakage magnetic feature is the data at a sampling moment in one cycle, and each time a fault diagnosis is performed, 20 groups of leakage magnetic data of path L1 in one voltage cycle are collected (each group has 15 points of data).

[0075] Step 103, based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle, determine the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located.

[0076] Step 104 , based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group, determine the target coil having a short circuit fault in the coil arrangement sequence of the target observation path.

[0077] Next, based on the flux density component value group at multiple preset time intervals within the preset voltage cycle, the peak value of the flux density component of the target observation path and the target flux density component value group where the peak value of the flux density component is located are determined. Based on the storage position of the peak value of the flux density component in the target flux density component value group, the target coil with a short circuit fault is determined in the coil arrangement order of the target observation path. Traditional transformer protection measures cannot quickly and sensitively judge transformer coil-related faults. Leakage magnetic flux is an intermediate product of energy conversion when the transformer is working. When the coil fails, the transformer leakage magnetic field will change rapidly. By performing local analysis on the measurement point data on a suitable observation path, it is found that there is a strong correlation feature of the leakage magnetic change after the transformer inter-turn short circuit fault. By further mining this feature and using the peak search algorithm, the transformer fault diagnosis is achieved through leakage magnetic data.

[0078] By applying the technical solution of this embodiment, within the preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target collection axis is collected according to the preset time interval, wherein the magnetic flux density component value of each coil collected at the same preset time interval is stored in the same magnetic flux density component value group according to the coil arrangement order; based on the magnetic flux density component value groups of multiple preset time intervals within the preset voltage cycle, the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located are determined; based on the storage position of the magnetic flux density component peak value in the target magnetic flux density component value group, the target coil with short circuit fault is determined in the coil arrangement order of the target observation path. The fault coil position can be specifically located to improve maintenance efficiency.

[0079] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another power transformer fault detection method is provided, such as Figure 4 As shown, the method includes:

[0080] Step 201, based on the type of the target power transformer that has a short circuit fault, determine the target observation path of the target power transformer and the target collection axis of the magnetic flux density component, wherein different types of power transformers correspond to preset observation paths and preset collection axes of the magnetic flux density component, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is the x-axis, the y-axis or the z-axis.

[0081] Step 202, within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target collection axis is collected at a preset time interval, wherein the magnetic flux density component values ​​of each coil collected at the same preset time interval are stored in the same magnetic flux density component value group according to the coil arrangement order.

[0082] Step 203, based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle, determine the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located.

[0083] Step 204 , based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group, determine the target coil having a short circuit fault in the coil arrangement sequence of the target observation path.

[0084] In the above embodiment of the present application, within the preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil target collection axis is collected according to the preset time interval, wherein the magnetic flux density component value of each coil collected at the same preset time interval is stored in the same magnetic flux density component value group according to the coil arrangement order; based on the magnetic flux density component value group of multiple preset time intervals within the preset voltage cycle, the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located are determined; based on the storage position of the magnetic flux density component peak value in the target magnetic flux density component value group, the target coil with short circuit fault is determined in the coil arrangement order of the target observation path. The fault coil position can be specifically located to improve maintenance efficiency.

[0085] Step 205, calculating the peak width based on the peak value of the magnetic flux density component and the peak width calculation formula, wherein the peak width calculation formula is:

[0086]

[0087] W represents the peak width, x1 and x2 represent the first process parameter and the second process parameter respectively, S max Indicates the peak value of the magnetic flux density component, S p and S p+1 Respectively represent the target magnetic flux density component value group that satisfies The pth and p+1th target magnetic flux density component values, S q and S q+1 Respectively represent the target magnetic flux density component value group that satisfies The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position number values ​​of the pth and p+1th target magnetic flux density component values ​​in the target magnetic flux density component value group, x q and x q+1 They respectively represent the position sequence values ​​of the qth and q+1th target magnetic flux density component values ​​in the target magnetic flux density component value group.

[0088] Step 206, among the multiple preset peak width range intervals corresponding to the target power transformer, determine the target peak width range interval that includes the peak width, and determine the number of turns in the target coil where short circuit faults occur based on the preset number of turns corresponding to the target peak width range interval, wherein different preset peak width range intervals correspond to different preset numbers of turns where short circuit faults occur.

[0089] Next, the peak width is calculated based on the peak value of the magnetic flux density component and the peak width calculation formula. Specifically, the peak value of the magnetic flux density component is S max , first calculate the "three-quarters high value", that is:

[0090]

[0091] Next, traverse the target magnetic flux density component value group S N , N represents the total number of coils. When N is 17, S N =[S1,S2,S i ,......S 17 ] Find satisfaction and The two intervals are located on the left and right sides of the peak respectively. Let the left interval be: (x left ,S left )=(x p ,S p ) and (x p+1 ,S p+1 ), the right interval is: (x right ,S right )=(x q ,S q ) and (x q+1 ,S q+1 ), calculate the first process parameter x1 and the second process parameter x2, and finally, the peak width W = x2-x1.

[0092] Among the multiple preset peak width range intervals corresponding to the target power transformer, the target peak width range interval including the peak width is determined, and based on the preset number of turns corresponding to the target peak width range interval, the number of turns in the target coil where short circuit faults occur is determined. The short circuit occurs because the insulation layer is damaged, and the calculated peak width is proportional to the number of short-circuited turns. The target peak width range interval can be set as follows: when two turns of the coil are short-circuited, the peak width is 22.7, and the target peak width range interval for the two-turn short circuit is set based on 22.7, while the peak width for the four-turn short circuit is 35.7, that is, the target peak width range interval for the four-turn short circuit is set based on 35.7.

[0093] By applying the technical solution of the present embodiment, within the preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target collection axis is collected according to the preset time interval, wherein the magnetic flux density component value of each coil collected at the same preset time interval is stored in the same magnetic flux density component value group according to the coil arrangement order; based on the magnetic flux density component value groups of multiple preset time intervals within the preset voltage cycle, the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located are determined; based on the storage position of the magnetic flux density component peak value in the target magnetic flux density component value group, the target coil with a short circuit fault is determined in the coil arrangement order of the target observation path. The peak width is calculated based on the peak value of the flux density component and the peak width calculation formula. The target peak width range interval including the peak width is determined in the multiple preset peak width range intervals corresponding to the target power transformer. Based on the preset number of turns corresponding to the target peak width range interval, the number of turns where short-circuit faults occur in the target coil is determined. By detecting early turn-to-turn short-circuit faults and taking measures in advance, the losses caused by the faults can be effectively reduced.

[0094] Further, as a refinement and expansion of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another power transformer fault detection method is provided, such as Figure 5 As shown, the method includes:

[0095] Step 301, based on the type of the target power transformer that has a short circuit fault, determine the target observation path of the target power transformer and the target collection axis of the magnetic flux density component, wherein different types of power transformers correspond to preset observation paths and preset collection axes of the magnetic flux density component, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is the x-axis, the y-axis or the z-axis.

[0096] In the above embodiment of the present application, based on the type of the target power transformer with a short circuit fault, the target observation path of the target power transformer and the target acquisition axis of the magnetic flux density component are determined to prepare for subsequent data collection.

[0097] Step 302, within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target collection axis is collected at a preset time interval, wherein the magnetic flux density component values ​​of each coil collected at the same preset time interval are stored in the same magnetic flux density component value group according to the coil arrangement order.

[0098] Step 303: Determine multiple sliding window sizes based on the total number of line cakes and a sliding window size determination formula.

[0099] Step 304, for any magnetic flux density component value group within a preset voltage cycle, select any sliding window scale, and determine the magnetic flux density component scale analysis value group of the magnetic flux density component value group under the selected sliding window scale according to the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, wherein the magnetic flux density component scale analysis value determination formula is:

[0100]

[0101] a k,i Indicates the i-th magnetic flux density component value S in the magnetic flux density component value group under the sliding window scale k i Corresponding magnetic flux density component scale analysis values.

[0102] Step 305: construct a magnetic flux density component scale analysis value matrix based on the magnetic flux density component value group at various sliding window scales.

[0103] Step 306, in the magnetic flux density component scale analysis value matrix, based on the matrix column with the most occurrences of the value 1, determine the target storage position of the candidate magnetic flux density component peak value in the magnetic flux density component value group, and determine the candidate magnetic flux density component peak value in the magnetic flux density component value group based on the target storage position, wherein the sliding window scale determination formula is:

[0104] L=[N / 2]-1,k∈[1,2,3,...L],

[0105] k represents the sliding window size, which is a natural number greater than or equal to 1 and less than or equal to the third process parameter L, [N / 2] represents the smallest integer less than or equal to N / 2, and N represents the total number of line cakes.

[0106] Step 307: for each of the candidate magnetic flux density component peak value groups, determine the minimum value among the absolute values ​​of the candidate magnetic flux density component peak value as the magnetic flux density component peak value of the target observation path.

[0107] Step 308: determining a target magnetic flux density component value group where the magnetic flux density component peak value is located based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle.

[0108] Step 309 , based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group, determine the target coil having a short circuit fault in the coil arrangement sequence of the target observation path.

[0109] Next, within the preset voltage cycle corresponding to the target power transformer, for each wire coil in the target observation path, the magnetic flux density component value of each wire coil in the target acquisition axis is collected at a preset time interval, and multiple sliding window scales are determined based on the total number of wire coils and the sliding window scale determination formula. For any magnetic flux density component value group within the preset voltage cycle, any sliding window scale is selected, and the magnetic flux density component scale analysis value group of the magnetic flux density component value group under the selected sliding window scale is determined according to the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, and the magnetic flux density component scale analysis value matrix is ​​constructed based on the magnetic flux density component scale analysis value group of the magnetic flux density component value group under various sliding window scales. In the flux density component scale analysis value matrix, based on the matrix column with the most occurrences of the value 1, the target storage position of the candidate flux density component peak value in the flux density component value group is determined, and based on the target storage position, the candidate flux density component peak value is determined in the flux density component value group, and for the candidate flux density component peak value of each flux density component value group, the minimum value among the absolute values ​​of the candidate flux density component peak value is determined as the flux density component peak value of the target observation path. Based on the flux density component value group of multiple preset time intervals within the preset voltage cycle, the target flux density component value group where the flux density component peak value is located is determined. Based on the storage position of the flux density component peak value in the target flux density component value group, the target coil having a short circuit fault is determined in the coil arrangement order of the target observation path.

[0110] Specifically, any group of magnetic flux density component values ​​within a preset voltage cycle, for example:

[0111] S 17 =[2,5,1,6,7,3,8,12,5,10,4,9,11,2,7,3,6],

[0112] N = 17, L = [N / 2] - 1 = [17 / 2] - 1 = 7, so the sliding window size k = 1, 2, 3, 4, 5, 6, 7, a k,i Indicates the i-th magnetic flux density component value S in the magnetic flux density component value group under the sliding window scale k i Corresponding magnetic flux density component scale analysis values.

[0113] For each sliding window size k, and the magnetic flux density component value set S N Each S i , i represents the magnetic flux density component value group S N If the sequence number in S i-k >S i ∧S i >S i+k , then a k,iis 1 if the value is true, otherwise it is 0.

[0114] To this end, when the sliding window size k = 1, a 1,i =[0,1,0,0,1,0,1,1,0,1,01,1,0,1,0,0](magnetic flux density component scale analysis value group), finally, calculate the magnetic flux density component scale analysis value group a under various sliding window scales k 1,i 、a 2,i 、a 3,i 、a 4,i 、a 5,i 、a 6,i 、a 7,i , construct the magnetic flux density component scale analysis value matrix A:

[0115]

[0116] In the flux density component scale analysis value matrix A, the value 1 appears the most times in the 7th column, so it can be finally determined that the 7th coil has a fault.

[0117] By applying the technical solution of the present embodiment, based on the type of the target power transformer that has a short-circuit fault, the target observation path of the target power transformer and the target collection axis of the magnetic flux density component are determined. Within the preset voltage period corresponding to the target power transformer, for each wire coil in the target observation path, the magnetic flux density component value of each wire coil on the target collection axis is collected at a preset time interval. Based on the total number of wire coils and the sliding window scale determination formula, multiple sliding window scales are determined. For any magnetic flux density component value group within the preset voltage period, any sliding window scale is selected. According to the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, the magnetic flux density component scale analysis value group of the magnetic flux density component value group under the selected sliding window scale is determined. Based on the magnetic flux density component scale analysis value group of the magnetic flux density component value group under various sliding window scales, a magnetic flux density component scale analysis value matrix is ​​constructed. In the flux density component scale analysis value matrix, based on the matrix column with the most occurrences of the value 1, the target storage position of the candidate flux density component peak value in the flux density component value group is determined, and based on the target storage position, the candidate flux density component peak value is determined in the flux density component value group, and for the candidate flux density component peak value of each flux density component value group, the minimum value among the absolute values ​​of the candidate flux density component peak value is determined as the flux density component peak value of the target observation path. Based on the flux density component value group of multiple preset time intervals within the preset voltage cycle, the target flux density component value group where the flux density component peak value is located is determined. Based on the storage position of the flux density component peak value in the target flux density component value group, the target wire cake with a short circuit fault is determined in the wire cake arrangement order of the target observation path. The fault wire cake position can be specifically located to improve maintenance efficiency.

[0118] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a power transformer fault detection device, such as Figure 6 As shown, the device comprises:

[0119] The observation path determination module 401 is used to determine the target observation path of the target power transformer and the target collection axis of the magnetic flux density component based on the type of the target power transformer with a short circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes of the magnetic flux density component, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is the x-axis, the y-axis or the z-axis;

[0120] The magnetic flux density component acquisition module 402 is used to acquire the magnetic flux density component value of each wire cake on the target acquisition axis according to a preset time interval for each wire cake in the target observation path within a preset voltage cycle corresponding to the target power transformer, wherein the magnetic flux density component value of each wire cake acquired at the same preset time interval is stored in the same magnetic flux density component value group according to the arrangement order of the wire cakes;

[0121] A magnetic flux density component peak value determination module 403, for determining the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle;

[0122] The short circuit fault detection module 404 is used to determine the target coil having a short circuit fault in the coil arrangement sequence of the target observation path based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group.

[0123] Optionally, the magnetic flux density component peak value determination module 403 is further used to:

[0124] Based on the total number of line cakes and the sliding window scale determination formula, multiple sliding window scales are determined;

[0125] For any magnetic flux density component value group within a preset voltage cycle, obtaining a magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales;

[0126] Based on the magnetic flux density component scale analysis value group, the candidate magnetic flux density component peak value of the magnetic flux density component value group is determined, and based on the candidate magnetic flux density component peak value of each magnetic flux density component value group, the magnetic flux density component peak value of the target observation path is determined, wherein the sliding window scale determination formula is:

[0127] L=[N / 2]-1,k∈[1,2,3,...L],

[0128] k represents the sliding window size, which is a natural number greater than or equal to 1 and less than or equal to the third process parameter L, [N / 2] represents the smallest integer less than or equal to N / 2, and N represents the total number of line cakes.

[0129] Optionally, the magnetic flux density component peak value determination module 403 is further used to:

[0130] Select any sliding window scale, and determine the magnetic flux density component scale analysis value group of the magnetic flux density component value group under the selected sliding window scale according to the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, wherein the magnetic flux density component scale analysis value determination formula is:

[0131]

[0132] a k,i Indicates the i-th magnetic flux density component value S in the magnetic flux density component value group under the sliding window scale k i Corresponding magnetic flux density component scale analysis values.

[0133] Optionally, the magnetic flux density component peak value determination module 403 is further used to:

[0134] Based on the magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales, constructing a magnetic flux density component scale analysis value matrix;

[0135] In the flux density component scale analysis value matrix, the target storage position of the candidate flux density component peak point value in the flux density component value group is determined based on the matrix column where the value 1 appears the most times, and the candidate flux density component peak point value is determined in the flux density component value group based on the target storage position.

[0136] Optionally, the magnetic flux density component peak value determination module 403 is further used to:

[0137] For each candidate magnetic flux density component peak value of each magnetic flux density component value group, the minimum value among the absolute values ​​of the candidate magnetic flux density component peak value is determined as the magnetic flux density component peak value of the target observation path.

[0138] Optionally, the coil includes a plurality of turns of coil, and the short circuit fault detection module 404 is further used for:

[0139] Based on the peak value of the magnetic flux density component and the peak width calculation formula, the peak width is calculated, and based on the peak width, the number of turns with short circuit faults in the target coil is determined, wherein the peak width calculation formula is:

[0140]

[0141] W represents the peak width, x1 and x2 represent the first process parameter and the second process parameter respectively, S max Indicates the peak value of the magnetic flux density component, S p and S p+1 Respectively represent the target magnetic flux density component value group that satisfies The pth and p+1th target magnetic flux density component values, S q and S q+1 Respectively represent the target magnetic flux density component value group that satisfies The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position number values ​​of the pth and p+1th target magnetic flux density component values ​​in the target magnetic flux density component value group, x q and x q+1 They respectively represent the position sequence values ​​of the qth and q+1th target magnetic flux density component values ​​in the target magnetic flux density component value group.

[0142] Optionally, the short circuit fault detection module 404 is further configured to:

[0143] Among multiple preset peak width range intervals corresponding to the target power transformer, a target peak width range interval including the peak width is determined, and based on the preset number of turns corresponding to the target peak width range interval, the number of turns in which short circuit faults occur in the target coil is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns in which short circuit faults occur.

[0144] It should be noted that for other corresponding descriptions of the functional units involved in the power transformer fault detection device provided in the embodiment of the present application, reference can be made to Figure 1 , Figure 4 and Figure 5 The corresponding description in the method will not be repeated here.

[0145] Based on the above Figure 1 , Figure 4 and Figure 5 The method shown in the embodiment of the present application is accordingly provided with a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned Figure 1 , Figure 4 and Figure 5 The power transformer fault detection method shown.

[0146] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0147] Based on the above Figure 1 , Figure 4 and Figure 5 The method shown, and Figure 6 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides a computer device, which can be a personal computer, a server, a network device, etc. The computer device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 , Figure 4 and Figure 5 The power transformer fault detection method shown.

[0148] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0149] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not limit the computer device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0150] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and saves the hardware and software resources of the computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between the components inside the storage medium, and communication with other hardware and software in the physical device.

[0151] Through the description of the above implementation methods, the technicians in this field can clearly understand that the present application can be implemented by means of software plus necessary general hardware platforms, or can be implemented by hardware. Within the preset voltage cycle corresponding to the target power transformer, for each wire cake in the target observation path, the magnetic flux density component value of each wire cake in the target collection axis is collected according to the preset time interval, wherein the magnetic flux density component value of each wire cake collected at the same preset time interval is stored in the same magnetic flux density component value group according to the arrangement order of the wire cakes; based on the magnetic flux density component value group of multiple preset time intervals within the preset voltage cycle, the peak value of the magnetic flux density component of the target observation path and the target magnetic flux density component value group where the peak value of the magnetic flux density component is located are determined; based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group, the target wire cake with a short circuit fault is determined in the arrangement order of the wire cakes of the target observation path. It can specifically locate the faulty wire cake and improve the maintenance efficiency.

[0152] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0153] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for detecting a power transformer fault, characterized in that: The method comprises: Based on the type of the target power transformer with a short-circuit fault, determine the target observation path and the target collection axis of the magnetic flux density component of the target power transformer, wherein different types of power transformers correspond to preset observation paths and preset collection axes of the magnetic flux density component, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is the x-axis, the y-axis or the z-axis; Within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, the magnetic flux density component value of each coil in the target acquisition axis is collected at a preset time interval, wherein the magnetic flux density component values ​​of each coil collected at the same preset time interval are stored in the same magnetic flux density component value group according to the coil arrangement order; Based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle, determining the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located; Based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group, a target coil having a short circuit fault is determined in the coil arrangement sequence of the target observation path.

2. The method according to claim 1, characterized in that The coil includes a plurality of turns of coils. After determining a target coil having a short circuit fault in a coil arrangement sequence of a target observation path based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group, the method further includes: Based on the peak value of the magnetic flux density component and the peak width calculation formula, the peak width is calculated, and based on the peak width, the number of turns with short circuit faults in the target coil is determined, wherein the peak width calculation formula is: W=x2-x1, W represents the peak width, x1 and x2 represent the first process parameter and the second process parameter respectively, S max Indicates the peak value of the magnetic flux density component, S p and S p+1 Respectively represent the target magnetic flux density component value group that satisfies The pth and p+1th target magnetic flux density component values, S q and S q+1 Respectively represent the target magnetic flux density component value group that satisfies The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position number values ​​of the pth and p+1th target magnetic flux density component values ​​in the target magnetic flux density component value group, x q and x q+1 They respectively represent the position sequence values ​​of the qth and q+1th target magnetic flux density component values ​​in the target magnetic flux density component value group.

3. The method according to claim 2, characterized in that The step of determining the number of turns in the target coil having short circuit faults based on the peak width includes: Among multiple preset peak width range intervals corresponding to the target power transformer, a target peak width range interval including the peak width is determined, and based on the preset number of turns corresponding to the target peak width range interval, the number of turns in which short circuit faults occur in the target coil is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns in which short circuit faults occur.

4. The method according to claim 1, characterized in that The step of determining the peak value of the magnetic flux density component of the target observation path based on a group of magnetic flux density component values ​​at multiple preset time intervals within a preset voltage cycle includes: Based on the total number of line cakes and the sliding window scale determination formula, multiple sliding window scales are determined; For any magnetic flux density component value group within a preset voltage cycle, obtaining a magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales; Based on the magnetic flux density component scale analysis value group, the candidate magnetic flux density component peak value of the magnetic flux density component value group is determined, and based on the candidate magnetic flux density component peak value of each magnetic flux density component value group, the magnetic flux density component peak value of the target observation path is determined, wherein the sliding window scale determination formula is: L=[N / 2]-1,k∈[1,2,3,...L], k represents the sliding window size, which is a natural number greater than or equal to 1 and less than or equal to the third process parameter L, [N / 2] represents the smallest integer less than or equal to N / 2, and N represents the total number of line cakes.

5. The method according to claim 4, characterized in that The obtaining of the magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales comprises: Select any sliding window scale, and determine the magnetic flux density component scale analysis value group of the magnetic flux density component value group under the selected sliding window scale according to the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, wherein the magnetic flux density component scale analysis value determination formula is: a k,i Indicates the i-th magnetic flux density component value S in the magnetic flux density component value group under the sliding window scale k i Corresponding magnetic flux density component scale analysis values.

6. The method according to claim 5, characterized in that The step of determining the candidate magnetic flux density component peak point values ​​of the magnetic flux density component value group based on the magnetic flux density component scale analysis value group comprises: Based on the magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales, constructing a magnetic flux density component scale analysis value matrix; In the flux density component scale analysis value matrix, the target storage position of the candidate flux density component peak point value in the flux density component value group is determined based on the matrix column where the value 1 appears the most times, and the candidate flux density component peak point value is determined in the flux density component value group based on the target storage position.

7. The method according to claim 4, characterized in that The determining of the peak value of the magnetic flux density component of the target observation path based on the candidate magnetic flux density component peak value of each magnetic flux density component value group includes: For each candidate magnetic flux density component peak value of each magnetic flux density component value group, the minimum value among the absolute values ​​of the candidate magnetic flux density component peak value is determined as the magnetic flux density component peak value of the target observation path.

8. A power transformer fault detection device, characterized in that: The device comprises: An observation path determination module is used to determine a target observation path and a target collection axis of a magnetic flux density component of the target power transformer based on the type of the target power transformer with a short circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes of magnetic flux density components, respectively, the preset observation path is located inside the power transformer, a plurality of coils are arranged on the preset observation path, and the preset collection axis is an x-axis, a y-axis or a z-axis; A magnetic flux density component acquisition module is used to acquire the magnetic flux density component value of each wire cake in the target acquisition axis according to a preset time interval within a preset voltage cycle corresponding to the target power transformer, for each wire cake in the target observation path, wherein the magnetic flux density component value of each wire cake acquired at the same preset time interval is stored in the same magnetic flux density component value group according to the arrangement order of the wire cakes; A magnetic flux density component peak value determination module, used to determine the magnetic flux density component peak value of the target observation path and the target magnetic flux density component value group where the magnetic flux density component peak value is located based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle; The short circuit fault detection module is used to determine the target coil having a short circuit fault in the coil arrangement sequence of the target observation path based on the storage position of the peak value of the magnetic flux density component in the target magnetic flux density component value group.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting a fault in a power transformer as claimed in any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method for detecting a fault in a power transformer as claimed in any one of claims 1 to 7 is implemented.

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