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

Abstract

The present application discloses a power transformer fault detection method and apparatus, a storage medium, and a computer device. The method comprises: within a preset voltage cycle corresponding to a target power transformer, for each coil in a target observation path, collecting magnetic flux density component values ​​along a target collection axis of each coil at preset time intervals, 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 the 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 in which the magnetic flux density component peak value is located; and based on the storage position of the magnetic flux density component peak value in the target magnetic flux density component value group, determining the target coil with a short circuit fault in the coil arrangement order of the target observation path. This method can specifically locate the faulty coil and improve maintenance efficiency.

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 crucial equipment in power systems. With improved living standards, people are placing higher demands on both electricity demand and reliability. Transformer failures can cause widespread power outages, severely impacting people's lives and production. Currently, transformer failures, especially short-circuit failures, can lead to serious accidents if not addressed promptly. Summary of the Invention

[0003] In view of this, the present application provides a power transformer fault detection method and apparatus, storage medium, and computer equipment. Within a preset voltage cycle corresponding to a target power transformer, the method collects magnetic flux density component values ​​along the target collection axis of each coil at preset time intervals for each coil in a target observation path. The magnetic flux density component values ​​collected for each coil at the same preset time interval are stored in the same magnetic flux density component value group in the order in which the coils are arranged. Based on the magnetic flux density component value groups at 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 in which the magnetic flux density component peak value lies 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 within the coil arrangement sequence of the target observation path. This method can specifically locate the faulty coil and 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] Determining a target observation path and a target collection axis for a magnetic flux density component of the target power transformer based on the type of the target power transformer having the short-circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes for 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 an x-axis, a y-axis, or a z-axis.

[0006] Within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, magnetic flux density component values ​​of each coil along the target acquisition axis are collected at preset time intervals, 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] Determining, based on a plurality of magnetic flux density component value groups at preset time intervals within a preset voltage cycle, a magnetic flux density component peak value of the target observation path and a target magnetic flux density component value group in which the magnetic flux density component peak value is located;

[0008] 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 having a short circuit fault is determined in the coil arrangement sequence of the target observation path.

[0009] Optionally, 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:

[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 between 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] constructing a magnetic flux density component scale analysis value matrix based on the magnetic flux density component value group at various sliding window scales;

[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 in which 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 magnetic flux density component peak value 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 multiple 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] The peak width is calculated based on the peak value of the magnetic flux density component and the peak width calculation formula. The number of turns in the target coil having short circuit faults is determined based on the peak width. 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, satisfying 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, satisfying The qth and q+1th target magnetic flux density component values, x p and xp+1 Respectively represent the position sequence 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 the target coil where short circuit faults occur is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns where 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, configured to determine a target observation path and a target collection axis for a magnetic flux density component of a target power transformer based on the type of the target power transformer experiencing the short-circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes for 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 configured to acquire, for each coil in the target observation path, a magnetic flux density component value of each coil along the target acquisition axis at a preset time interval within a preset voltage cycle corresponding to the target power transformer, wherein the magnetic flux density component values ​​of each coil acquired at the same preset time interval are stored in the same magnetic flux density component value group in the order in which the coils are arranged;

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

[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 configured 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 between 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 configured 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 configured to:

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

[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 in which 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 configured 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 bobbin includes a multi-turn coil, and the short-circuit fault detection module is further configured to:

[0051] The peak width is calculated based on the peak value of the magnetic flux density component and the peak width calculation formula. The number of turns in the target coil having short circuit faults is determined based on the peak width. 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, satisfying 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, satisfying The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position sequence 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 configured 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 the target coil where short circuit faults occur is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns where 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. 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 solution, the present application provides a power transformer fault detection method and apparatus, storage medium, and computer equipment. Within a preset voltage cycle corresponding to a target power transformer, the method collects magnetic flux density component values ​​along the target collection axis of each coil in a target observation path at preset time intervals for each coil. The magnetic flux density component values ​​collected for each coil at the same preset time interval are stored in the same magnetic flux density component value group in the order in which the coils are arranged. Based on the magnetic flux density component value groups at 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 in which the magnetic flux density component peak value lies 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 within the coil arrangement sequence of the target observation path. This method can specifically locate the faulty coil and 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 showing 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, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

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

[0069] Step 101: Determine a target observation path and a target collection axis for a magnetic flux density component of the target power transformer based on the type of the target power transformer experiencing a short circuit fault. Different types of power transformers have corresponding preset observation paths and preset collection axes for magnetic flux density components. The preset observation path is located inside the power transformer, and a plurality of coils are arranged on the preset observation path. 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 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] During operation, a transformer transmits energy through the alternating magnetic field of its windings. Therefore, when a winding fault occurs, the leakage magnetic field changes. Even a relatively small short circuit can significantly alter the transformer's leakage magnetic field characteristics. Transformer leakage magnetic field data (magnetic flux density components) can reflect the transformer winding condition. Therefore, studying the distribution characteristics of transformer winding leakage magnetic field to investigate early-stage transformer winding faults not only overcomes the limitations of traditional methods in terms of speed and sensitivity, but also enriches transformer fault diagnosis methods. Historical data shows that winding faults are the most frequent type of transformer fault. Among winding faults, interturn short circuits are the most common and are gradually increasing in severity. Therefore, early detection of early-stage interturn short circuit faults and proactive measures can effectively reduce the losses caused by these faults.

[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 acquisition axis of the magnetic flux density component are determined. Different types of power transformers correspond to preset observation paths and preset acquisition axes of the magnetic flux density component. The preset observation path is located inside the power transformer, and the preset acquisition 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. The path height is 800mm, the coil height is 300mm, the inner winding has 15 coils, and each coil is 20mm high. The coils are numbered 1 to 15 from bottom to top. Regarding the selection of the preset observation path, the normal operating conditions of the transformer can be simulated and the magnetic flux density modulus of the three observation paths can be plotted. The path with the largest magnetic flux density modulus value among the three observation paths is selected as the preset observation path. The magnetic flux density modulus is composed of the three vector fields x, y, and z. The magnetic sensor also measures magnetic vectors, so analyzing different magnetic field vectors is more practical. Selecting the axis with the largest component value can reflect obvious fault changes. For example, the target acquisition axis for the magnetic flux density component of the aforementioned oil-immersed transformer can be set to the z-axis. Specifically, 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, collects a magnetic flux density component value of each coil along the target collection axis at a preset time interval. 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 in the order in which the coils are arranged.

[0074] During transformer operation, the magnetic field also changes periodically, with the change cycle being synchronized with the voltage. Analysis of the transformer's magnetic leakage curve revealed that in a short-circuit state, the curve change at the fault location is always greater than in a normal state (the rise is large, forming a convex shape, and the decrease is large, forming a concave shape). This indicates that within a cycle of a turn-to-turn short-circuit in the transformer, there are always unique waveform moments. To this end, within the preset voltage cycle corresponding to the target power transformer, the magnetic flux density components corresponding to each coil in the target observation path are collected at preset time intervals. The target magnetic flux density component value in the axial direction is collected. The magnetic flux density component values ​​for each coil collected at the same preset time interval are stored in the same magnetic flux density component value group in the order in which the coils are arranged. 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 AC frequency, 50 Hz, that is, the change cycle is 0.02 seconds, and 20 samples can be sampled within one cycle. The proposed leakage magnetic feature is the data at a sampling moment within one cycle. Each time fault diagnosis is performed, 20 groups of leakage magnetic data of path L1 in one voltage cycle are collected (each group has data of 15 points).

[0075] Step 103 : 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.

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

[0077] Next, based on the magnetic flux density component value group at multiple preset time intervals within a 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 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. Traditional transformer protection measures are unable to 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 of the measurement point data on a suitable observation path, a strong correlation feature of the leakage magnetic flux change after the transformer inter-turn short circuit fault is found. By further exploring this feature and using the peak search algorithm, transformer fault diagnosis is achieved through leakage magnetic flux data.

[0078] By applying the technical solution of this embodiment, within a preset voltage cycle corresponding to a target power transformer, magnetic flux density component values ​​along the target acquisition axis of each coil in a target observation path are collected at preset time intervals. The magnetic flux density component values ​​collected for each coil 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 the preset voltage cycle, the peak value of the magnetic flux density component in the target observation path and the target magnetic flux density component value group in which the peak value lies 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 coil with a short circuit fault is determined within the coil arrangement order of the target observation path. This allows for specific location of the faulty coil, improving maintenance efficiency.

[0079] Furthermore, 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 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 a target observation path and a target collection axis for the magnetic flux density component of the target power transformer. Different types of power transformers correspond to preset observation paths and preset collection axes for the magnetic flux density component, respectively. The preset observation path is located inside the power transformer, and multiple coils are arranged on the preset observation path. The preset collection axis is the x-axis, y-axis, or z-axis.

[0081] Step 202 , within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, collect the magnetic flux density component value of each coil along the target collection axis at a preset time interval. 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 in the order in which the coils are arranged.

[0082] Step 203 : 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.

[0083] Step 204 : Based on the storage position of the magnetic flux density component peak value 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.

[0084] In the above embodiment of the present application, within a preset voltage cycle corresponding to the target power transformer, the magnetic flux density component value along the target acquisition axis of each coil in the target observation path is collected at preset time intervals. 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 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 in which 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 coil with a short circuit fault is determined in the coil arrangement order of the target observation path. This can specifically locate the faulty coil and improve maintenance efficiency.

[0085] Step 205: Calculate 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, satisfying 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, satisfying The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position sequence 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: Determine a target peak width range interval that includes the peak width among multiple preset peak width range intervals corresponding to the target power transformer, 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 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. The calculated peak width is directly 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. The target peak width range interval for the two-turn short circuit is set based on 22.7, and 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 this embodiment, 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 a target collection axis is collected at preset time intervals, 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 in the coil arrangement order. Based on the magnetic flux density component value groups at 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 in which 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 coil having a short circuit fault is determined in the coil arrangement order of the target observation path. Based on the peak value of the magnetic flux density component and the peak width calculation formula, the peak width is calculated. Among the multiple preset peak width range intervals corresponding to the target power transformer, the target peak width range interval that includes the peak width is determined. 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. By detecting early-stage turn-to-turn short-circuit faults and taking measures in advance, the losses caused by the faults can be effectively reduced.

[0094] Furthermore, 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: Determine a target observation path and a target collection axis for a magnetic flux density component of the target power transformer based on the type of the target power transformer experiencing a short circuit fault. Different types of power transformers have corresponding preset observation paths and preset collection axes for magnetic flux density components. The preset observation path is located inside the power transformer, and multiple coils are arranged on the preset observation path. The preset collection axis is the x-axis, y-axis, or 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, magnetic flux density component values ​​of each coil along the target acquisition axis are collected at preset time intervals. 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 in the order in which the coils are arranged.

[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 based on 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 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 : constructing a magnetic flux density component scale analysis value matrix based on the magnetic flux density component scale analysis value group of 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 location 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 location, 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 between 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 candidate magnetic flux density component peak value of each magnetic flux density component value group, 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 magnetic flux density component peak value in the target magnetic flux density component value group, determine the target coil having the short circuit fault in the coil arrangement sequence of the target observation path.

[0109] Next, within a preset voltage cycle corresponding to the target power transformer, the magnetic flux density component values ​​of each coil in the target observation path along the target acquisition axis are collected at preset time intervals. Multiple sliding window scales are determined based on the total number of coils and a sliding window scale determination formula. For any group of magnetic flux density component values ​​within the preset voltage cycle, any sliding window scale is selected. Based on the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, a magnetic flux density component scale analysis value group for the magnetic flux density component value group at the selected sliding window scale is determined. A magnetic flux density component scale analysis value matrix is ​​constructed based on the magnetic flux density component scale analysis value groups for the magnetic flux density component value group at various sliding window scales. In the magnetic flux density component scale analysis value matrix, the target storage location of the candidate magnetic flux density component peak value in the magnetic flux density component value group is determined based on the matrix column with the most occurrences of the value 1. The candidate magnetic flux density component peak value is then determined in the magnetic flux density component value group based on the target storage location. For each candidate magnetic flux density component peak value in 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 for the target observation path. Based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle, the target magnetic flux density component value group containing the magnetic flux density component peak value is determined. Based on the storage location of the magnetic flux density component peak value in the target magnetic flux density component value group, the target coil having a short circuit fault is determined in the coil arrangement sequence of the target observation path.

[0110] Specifically, any magnetic flux density component value group 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 group S N Each S in i , i represents the magnetic flux density component value group S N In the sequence number, if S i-k >S i ∧S i >S i+k , then a k,iis 1 if the value is set, otherwise it is 0.

[0114] Therefore, 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 magnetic flux density component scale analysis value matrix A, the number 1 appears the most times in the 7th column. Therefore, it can be finally determined that the 7th coil has a fault.

[0117] By applying the technical solution of this embodiment, based on the type of target power transformer that has a short-circuit fault, a target observation path and a target collection axis of the magnetic flux density component of the target power transformer are determined. 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 on the target collection axis is collected at preset time intervals. Based on the total number of coils and a sliding window scale determination formula, multiple sliding window scales are determined. For any magnetic flux density component value group within the preset voltage cycle, any sliding window scale is selected. Based on the magnetic flux density component scale analysis value determination formula and the magnetic flux density component value group, a magnetic flux density component scale analysis value group for the magnetic flux density component value group at the selected sliding window scale is determined. Based on the magnetic flux density component scale analysis value groups for the magnetic flux density component value group at various sliding window scales, a magnetic flux density component scale analysis value matrix is ​​constructed. In the magnetic flux density component scale analysis value matrix, the target storage location of the candidate magnetic flux density component peak value in the magnetic flux density component value group is determined based on the matrix column with the most occurrences of the value 1. The candidate magnetic flux density component peak value is then determined in the magnetic flux density component value group based on the target storage location. For each candidate magnetic flux density component peak value in 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. Based on the magnetic flux density component value groups at multiple preset time intervals within a preset voltage cycle, the target magnetic flux density component value group containing the magnetic flux density component peak value is determined. Based on the storage location 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 sequence of the target observation path. This can specifically locate the faulty coil, improving 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 includes:

[0119] An observation path determination module 401 is configured to determine a target observation path and a target collection axis for a magnetic flux density component of a target power transformer based on the type of the target power transformer experiencing a short circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes for 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.

[0120] The magnetic flux density component acquisition module 402 is configured to acquire the magnetic flux density component value of each coil in the target observation path along the target acquisition axis at a preset time interval within a preset voltage cycle corresponding to the target power transformer. The magnetic flux density component values ​​of each coil acquired at the same preset time interval are stored in the same magnetic flux density component value group in the order in which the coils are arranged.

[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 in which 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 configured to determine a target coil having a short circuit fault in the coil arrangement sequence of the target observation path based on the storage position of the magnetic flux density component peak value in the target magnetic flux density component value group.

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

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

[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 in which 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 configured 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 multiple turns of coil, and the short circuit fault detection module 404 is further configured to:

[0139] The peak width is calculated based on the peak value of the magnetic flux density component and the peak width calculation formula. The number of turns in the target coil having short circuit faults is determined based on the peak width. 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, satisfying 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, satisfying The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position sequence 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 the target coil where short circuit faults occur is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns where 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 FIG. 1 is a method for performing the above-mentioned operation. Accordingly, the embodiment of the present application further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned operation is performed. Figure 1 、 Figure 4 and Figure 5 The power transformer fault detection method shown in FIG.

[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, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or 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 in FIG.

[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 understand that the computer device structure provided in this embodiment does not constitute a limitation on 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. An operating system is a program that manages and stores the hardware and software resources of a computer device, supporting the execution of information processing programs and other software and / or programs. The network communication module facilitates communication between components within the storage medium, as well as with other hardware and software within the physical device.

[0151] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by means of hardware. 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 at preset time intervals, 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 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 in which 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. This can specifically locate the faulty coil and improve maintenance efficiency.

[0152] Those skilled in the art will understand 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 required to implement the present application. Those skilled in the art will understand that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0153] The serial numbers of the above application are for descriptive purposes only and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosure only discloses several specific implementation scenarios of the present application, but the present application is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A method for detecting a power transformer fault, characterized in that: The method comprises: Determining a target observation path and a target collection axis for a magnetic flux density component of the target power transformer based on the type of the target power transformer having the short-circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes for 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 an x-axis, a y-axis, or a z-axis. Within a preset voltage cycle corresponding to the target power transformer, for each coil in the target observation path, magnetic flux density component values ​​of each coil along the target acquisition axis are collected at preset time intervals, 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; Determining, based on a plurality of magnetic flux density component value groups at preset time intervals within a preset voltage cycle, a magnetic flux density component peak value of the target observation path and a target magnetic flux density component value group in which the magnetic flux density component peak value is located; 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 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 multiple 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 magnetic flux density component peak value in the target magnetic flux density component value group, the method further includes: The peak width is calculated based on the peak value of the magnetic flux density component and the peak width calculation formula. The number of turns in the target coil having short circuit faults is determined based on the peak width. 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, satisfying 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, satisfying The qth and q+1th target magnetic flux density component values, x p and x p+1 Respectively represent the position sequence 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 determining, based on the peak width, the number of turns in the target coil where short circuit faults occur, 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 the target coil where short circuit faults occur is determined, wherein different preset peak width range intervals correspond to different preset numbers of turns where short circuit faults occur.

4. The method according to claim 1, wherein 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 a plurality of 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 between 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 a magnetic flux density component scale analysis value group of the magnetic flux density component value group at various sliding window scales includes: 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 determining, based on the magnetic flux density component scale analysis value group, candidate magnetic flux density component peak point values ​​of the magnetic flux density component value group comprises: constructing a magnetic flux density component scale analysis value matrix based on the magnetic flux density component value group at various sliding window scales; 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 in which 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 magnetic flux density component peak value 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, configured to determine a target observation path and a target collection axis for a magnetic flux density component of a target power transformer based on the type of the target power transformer experiencing the short-circuit fault, wherein different types of power transformers correspond to preset observation paths and preset collection axes for 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 configured to acquire, for each coil in the target observation path, a magnetic flux density component value of each coil along the target acquisition axis at a preset time interval within a preset voltage cycle corresponding to the target power transformer, wherein the magnetic flux density component values ​​of each coil acquired at the same preset time interval are stored in the same magnetic flux density component value group in the order in which the coils are arranged; a magnetic flux density component peak value determination module, configured to determine, based on a plurality of magnetic flux density component value groups at preset time intervals within a preset voltage cycle, a magnetic flux density component peak value of the target observation path and a target magnetic flux density component value group in which the magnetic flux density component peak value is located; 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 power transformer fault according to 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, wherein: When the processor executes the computer program, the method for detecting a power transformer fault according to any one of claims 1 to 7 is implemented.

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