Offline monitoring method and device for transformer winding state, equipment and storage medium

Abstract

The embodiment of the invention discloses an off-line monitoring method and device for the state of a transformer winding, equipment and a storage medium. The method comprises the steps of obtaining off-line frequency response test data, at least comprising a smooth frequency response curve, of a transformer; obtaining a resonance frequency and a resonance peak value according to the smooth frequency response curve, and calculating a first transformer capacity, a first no-load loss and a first short-circuit loss under the resonance frequency; utilizing the first transformer capacity, the first no-load loss, the first short-circuit loss and a preset multi-dimensional inferior fusion model to obtain a first comprehensive index used for reflecting the performance change of the transformer winding under the resonant frequency; and determining the current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index. By means of the mode, higher sensitivity and reliability can be displayed in diagnosis of winding states such as winding deformation faults, and the method has important significance in prolonging the service life of a transformer and improving the operation reliability and economical efficiency of a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment fault diagnosis, and in particular to an off-line monitoring method and device, equipment and storage medium for transformer winding status. Background Art

[0002] Transformers play a vital role in the power system. Their main function is to change the voltage level to meet the needs of power transmission and distribution. In high-voltage transmission lines, transformers are used to increase the voltage generated by generators to reduce power loss during transmission; while in distribution networks, transformers are used to reduce high voltage to a voltage level suitable for home and industrial use. This makes it possible to transmit electrical energy over long distances. By increasing the voltage, transformers reduce the current in the transmission line, thereby reducing power loss caused by resistance.

[0003] Once a winding failure occurs, the transformer needs to be inspected and further repaired, which will bring huge economic losses. Effective monitoring methods can reduce the occurrence of such failures, thereby reducing economic losses. Strengthening the monitoring of the working status of the transformer and timely discovering equipment failures are of great significance for extending the service life of the transformer and improving the reliability and economy of power grid operation. The offline experimental verification method shows higher sensitivity and reliability in diagnosing winding deformation failures.

[0004] However, the current off-line experimental verification method still needs to be further improved to further improve the sensitivity and reliability in diagnosing winding deformation faults. Summary of the invention

[0005] The main purpose of the present invention is to provide an offline monitoring method and device, equipment and storage medium for transformer winding status, which can solve the lack of means in the prior art to further improve the sensitivity and reliability in diagnosing winding deformation faults.

[0006] To achieve the above object, the present invention provides a first aspect of an offline monitoring method for transformer winding status, the method comprising:

[0007] Acquire offline frequency response test data of the transformer, wherein the offline frequency response test data at least includes a smooth frequency response curve;

[0008] Obtaining the resonant frequency and the resonant peak value according to the smooth frequency response curve, and calculating the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency;

[0009] Using the first transformer capacity, the first no-load loss, the first short-circuit loss and a preset multi-dimensional inferiority fusion model, a first comprehensive index at the resonant frequency is obtained, where the first comprehensive index is used to reflect the change in the transformer winding performance;

[0010] The current winding state of the transformer is determined according to the first comprehensive index and a predetermined second comprehensive index, wherein the second comprehensive index is obtained when the transformer is in a normal operating state.

[0011] In a feasible implementation, the method further includes:

[0012] Performing a short-circuit test and a no-load test on the transformer in a normal operating state to obtain a second no-load loss and a second short-circuit loss in a normal operating state;

[0013] The second comprehensive index is obtained according to the second no-load loss, the second short-circuit loss, the rated capacity of the transformer and the multi-dimensional inferiority fusion model.

[0014] In a feasible implementation, determining the current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index includes:

[0015] If the first comprehensive index is less than or equal to the second comprehensive index, determining that the current winding state of the transformer is a normal working state;

[0016] If the first comprehensive index is greater than the second comprehensive index, it is determined that the current winding state of the transformer is an abnormal working state.

[0017] In a feasible implementation, the step of obtaining offline frequency response test data of the transformer includes:

[0018] Sending an output signal instruction to the sinusoidal signal source, wherein the output signal instruction is used to instruct the sinusoidal signal source to send a sinusoidal wave excitation signal and continuously change the frequency of the sinusoidal wave excitation signal, wherein the sinusoidal wave excitation signal is injected from the end screen of the bushing on the low-voltage side of the transformer;

[0019] The sine wave excitation signal is collected to obtain an injection signal at each frequency point; the output signal at each frequency point generated by the high-voltage side bushing of the transformer and after passing through the Rogowski coil, the common emitter amplifier circuit, and the Kalman filter is collected to obtain a response signal;

[0020] Using the injection signal and the response signal at each frequency point, a discrete frequency response curve of the transformer is drawn;

[0021] A piecewise cubic polynomial function is constructed using cubic spline interpolation to approximate a set of discrete data points, thereby transforming the discrete frequency response curve into a smooth frequency response curve.

[0022] In a feasible implementation, the discrete frequency response curve includes the following mathematical expression:

[0023]

[0024] Where A is the amplitude frequency response curve gain of the discrete frequency response curve, V 1 is the amplitude of the response signal after normalization, V 2 is the normalized amplitude of the injection signal.

[0025] In a feasible implementation, the multi-dimensional inferiority fusion model includes the following mathematical expression:

[0026]

[0027] Among them, ω 1 ,ω 2 ,ω 3 is the adjustment coefficient, I is the comprehensive index, Sn is the transformer capacity, Po is the no-load loss, and Pk is the short-circuit loss.

[0028] To achieve the above object, the second aspect of the present invention provides an offline monitoring device for transformer winding status, the device comprising:

[0029] Data acquisition module: used to acquire offline frequency response test data of the transformer, wherein the offline frequency response test data at least includes a smooth frequency response curve;

[0030] Parameter determination module: used for obtaining the resonant frequency and the resonant peak value according to the smooth frequency response curve, and calculating the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency;

[0031] A first index module: used to obtain a first comprehensive index at the resonant frequency by using the first transformer capacity, the first no-load loss, the first short-circuit loss and a preset multi-dimensional inferiority fusion model, wherein the first comprehensive index is used to reflect the change in the transformer winding performance;

[0032] A state determination module is used to determine the current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index, wherein the second comprehensive index is obtained when the transformer is in normal operating state.

[0033] In a feasible implementation, the device further includes:

[0034] Simulation test module: used to perform short-circuit test and no-load test on the transformer in normal operating state, and obtain the second no-load loss and the second short-circuit loss in normal operating state;

[0035] The second index module is used to obtain the second comprehensive index according to the second no-load loss, the second short-circuit loss, the rated capacity of the transformer and the multi-dimensional inferiority fusion model.

[0036] To achieve the above-mentioned purpose, the third aspect of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps shown in the first aspect and any feasible implementation method.

[0037] To achieve the above-mentioned purpose, the fourth aspect of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps shown in the first aspect and any feasible implementation method.

[0038] The embodiments of the present invention have the following beneficial effects:

[0039] The present invention provides an offline monitoring method for transformer winding status, the method comprising: obtaining offline frequency response test data of the transformer, the offline frequency response test data at least including a smooth frequency response curve; obtaining the resonant frequency and the resonant peak according to the smooth frequency response curve, and calculating the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency; using the first transformer capacity, the first no-load loss, the first short-circuit loss and the preset multi-dimensional inferiority fusion model, obtaining the first comprehensive index at the resonant frequency, the first comprehensive index is used to reflect the change in transformer winding performance; determining the current winding status of the transformer according to the first comprehensive index and a predetermined second comprehensive index, the second comprehensive index is obtained under the normal operating state of the transformer. Through the above method, higher sensitivity and reliability can be shown in diagnosing winding status such as winding deformation faults, which is of great significance for extending the service life of the transformer and improving the reliability and economy of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] in:

[0042] Figure 1 It is a flow chart of an offline monitoring method of transformer winding status in an embodiment of the present invention;

[0043] Figure 2 Another flow chart of an offline monitoring method for transformer winding status according to an embodiment of the present invention;

[0044] Figure 3It is a structural block diagram of an offline monitoring device for transformer winding status in an embodiment of the present invention;

[0045] Figure 4 4 is a structural block diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1 , Figure 1 The flowchart of an offline monitoring method for transformer winding status in an embodiment of the present invention is shown in FIG. 1 . The method can be applied to both a terminal and a server. The terminal can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, a tablet computer, and a laptop computer. The server can be implemented by an independent server or a server cluster composed of multiple servers. This embodiment is described by applying to a terminal as an example. Figure 1 The method shown includes the following steps:

[0048] 101. Obtain offline frequency response test data of a transformer, wherein the offline frequency response test data at least includes a smooth frequency response curve;

[0049] It should be noted that an offline frequency response test can be performed on the transformer to be tested, by injecting an excitation signal into the transformer bushing by controlling the signal source, and then receiving and collecting the response signal, and using the injected signal and the response signal to obtain a smooth frequency response curve, including but not limited to interpolating discrete frequency response data to obtain a smooth frequency response curve, and the interpolation method includes but is not limited to cubic spline interpolation and other interpolation methods.

[0050] 102. Obtain the resonant frequency and the resonant peak value according to the smooth frequency response curve, and calculate the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency;

[0051] 103. Obtain a first comprehensive index at the resonant frequency by using the first transformer capacity, the first no-load loss, the first short-circuit loss, and a preset multi-dimensional inferiority fusion model, wherein the first comprehensive index is used to reflect a change in the transformer winding performance;

[0052] Furthermore, the first transformer capacity S*, the first no-load loss Po*, the first short-circuit loss Pk* and a preset multi-dimensional inferiority fusion model are used to obtain a first comprehensive index I* at the resonant frequency. The first comprehensive index is used to reflect the performance change of the transformer winding, that is, the resonant frequency and the resonant peak are obtained according to the smooth frequency response curve, and the transformer capacity S*, the no-load loss Po*, and the short-circuit loss Pk* at the resonant frequency are calculated, and I* is calculated using the multi-dimensional inferiority fusion model.

[0053] 104. Determine a current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index, wherein the second comprehensive index is obtained when the transformer is in normal operating state.

[0054] Furthermore, the winding state is analyzed using the first comprehensive index during actual operation of the transformer. Specifically, the current winding state of the transformer is determined based on the first comprehensive index and a predetermined second comprehensive index, and the second comprehensive index is obtained when the transformer is in normal operating condition.

[0055] Exemplarily, the second comprehensive index I can be obtained through the following steps A01 to A02, and the above method further includes steps A01 to A02:

[0056] A01. Perform a short-circuit test and a no-load test on the transformer in normal operating state to obtain a second no-load loss and a second short-circuit loss in normal operating state;

[0057] A02. Obtain the second comprehensive index according to the second no-load loss, the second short-circuit loss, the rated capacity of the transformer and the multi-dimensional inferiority fusion model.

[0058] It should be noted that the second comprehensive index I is obtained by performing a short-circuit test and a no-load test on the transformer in normal operation and combining the transformer rated capacity Sn, no-load loss Po, and short-circuit loss Pk.

[0059] The present invention provides an offline monitoring method for transformer winding status, the method comprising: obtaining offline frequency response test data of the transformer, the offline frequency response test data at least including a smooth frequency response curve; obtaining the resonant frequency and the resonant peak according to the smooth frequency response curve, and calculating the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency; using the first transformer capacity, the first no-load loss, the first short-circuit loss and the preset multi-dimensional inferiority fusion model, obtaining the first comprehensive index at the resonant frequency, the first comprehensive index is used to reflect the change in transformer winding performance; determining the current winding status of the transformer according to the first comprehensive index and a predetermined second comprehensive index, the second comprehensive index is obtained under the normal operating state of the transformer. Through the above method, higher sensitivity and reliability can be shown in diagnosing winding status such as winding deformation faults, which is of great significance for extending the service life of the transformer and improving the reliability and economy of power grid operation.

[0060] See also Figure 2 , Figure 2 FIG. 1 is another flow chart of an offline monitoring method for transformer winding status according to an embodiment of the present invention. Figure 2 The methods shown include:

[0061] 201. Obtain offline frequency response test data of a transformer, wherein the offline frequency response test data at least includes a smooth frequency response curve;

[0062] It should be noted that step 201 and Figure 1 The contents of step 101 are similar, and are not described here to avoid repetition. For details, please refer to Figure 1 The content of step 101 is shown.

[0063] In a feasible implementation, step 201 may include steps B01 to B04:

[0064] B01. Sending an output signal instruction to a sinusoidal signal source, wherein the output signal instruction is used to instruct the sinusoidal signal source to send a sinusoidal wave excitation signal and continuously change the frequency of the sinusoidal wave excitation signal, wherein the sinusoidal wave excitation signal is injected from the end screen of the bushing on the low-voltage side of the transformer;

[0065] B02, collecting the sinusoidal wave excitation signal to obtain the injection signal at each frequency point; collecting the output signal at each frequency point generated by the high-voltage side bushing of the transformer and after passing through the Rogowski coil, the common emitter amplifier circuit, and the Kalman filter to obtain a response signal;

[0066] Exemplarily, the sinusoidal signal source can be a high-frequency, high-power sinusoidal signal source. First, the injection and perception of the excitation signal are verified: the upper computer generates an output signal instruction and transmits it to the high-frequency, high-power sinusoidal signal source, so that the signal source can emit a sinusoidal wave excitation signal with a power of 200W and a 1MHz. By continuously changing the frequency of the externally applied sinusoidal wave excitation source, the excitation signal is injected from the end screen of the bushing on the low-voltage side of the transformer, and the output signal at each frequency point generated by the bushing on the high-voltage side is collected by the computer after passing through the Rogowski coil, the common emitter amplifier circuit, and the Kalman filter.

[0067] B03. Draw a discrete frequency response curve of the transformer using the injection signal and the response signal at each frequency point;

[0068] B04. Use cubic spline interpolation to construct a piecewise cubic polynomial function to approximate a set of discrete data points, and transform the discrete frequency response curve into a smooth frequency response curve.

[0069] Furthermore, the frequency response curve is drawn and optimized: the discrete frequency response curve of the power transformer of 1kHz-1MHz is drawn using the collected injection signal and response signal. The formula is as follows:

[0070]

[0071] Where A is the gain of the amplitude frequency response curve, V 1 is the normalized amplitude of the transformer response signal, V 2 is the normalized amplitude of the input excitation signal;

[0072] In order to obtain a smooth curve, a piecewise cubic polynomial function is constructed using cubic spline interpolation to approximate a set of discrete data points and transform the discrete frequency response curve into a smooth frequency response curve.

[0073] 202. Obtain the resonant frequency and the resonant peak value according to the smooth frequency response curve, and calculate the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency;

[0074] 203. Obtain a first comprehensive index at the resonant frequency by using the first transformer capacity, the first no-load loss, the first short-circuit loss, and a preset multi-dimensional inferiority fusion model, wherein the first comprehensive index is used to reflect a change in the transformer winding performance;

[0075] It should be noted that steps 202 and 203 are Figure 1 The contents of steps 102 and 103 are similar, and are not described here to avoid repetition. For details, please refer to Figure 1 The contents of steps 102 and 103 are shown.

[0076] Exemplarily, the establishment of a multi-dimensional inferiority fusion model can be to perform a short-circuit test and a no-load test on a transformer in a normal operating state, and establish a multi-dimensional inferiority fusion model by combining the transformer capacity Sn, no-load loss Po, and short-circuit loss Pk, thereby defining the comprehensive index I as follows:

[0077]

[0078] Among them, ω 1 ,ω 2 ,ω 3 is the adjustment coefficient, which is calculated according to the transformer working environment and design requirements. I is the comprehensive index, which reflects the change of transformer winding performance. Sn is the transformer capacity. Po is the no-load loss and Pk is the short-circuit loss.

[0079] 204. If the first comprehensive index is less than or equal to the second comprehensive index, it is determined that the current winding state of the transformer is a normal working state, and the second comprehensive index is obtained when the transformer is in a normal working state;

[0080] 205. If the first comprehensive index is greater than the second comprehensive index, determine that the current winding state of the transformer is an abnormal working state.

[0081] It should be noted that the first comprehensive index I* is calculated by using the smooth frequency response curve actually measured and formula (2); the first comprehensive index I* is compared with the second comprehensive index I. When I*≤I, it indicates that the transformer winding is in a safe working state, then step 204 is executed and the winding state is continued to be monitored; when I*>I, it indicates that there is a serious problem with the transformer winding and the transformer needs to be repaired, then step 205 is executed and an abnormal warning can be output.

[0082] The present invention provides an offline monitoring method for transformer winding state, the method comprising: obtaining offline frequency response test data of the transformer, the offline frequency response test data at least including a smooth frequency response curve; obtaining the resonant frequency and the resonant peak according to the smooth frequency response curve, and calculating the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency; using the first transformer capacity, the first no-load loss, the first short-circuit loss and the preset multi-dimensional inferiority fusion model to obtain the first comprehensive index at the resonant frequency, the first comprehensive index is used to reflect the change in transformer winding performance; if the first comprehensive index is less than or equal to the second comprehensive index, it is determined that the current winding state of the transformer is a normal working state, and the second comprehensive index is obtained under the normal operating state of the transformer; if the first comprehensive index is greater than the second comprehensive index, it is determined that the current winding state of the transformer is an abnormal working state. Through the above method, an offline experimental verification method for transformer winding state monitoring sensing is disclosed. First, verify the injection and perception of the excitation signal, draw a discrete frequency response curve, use cubic spline interpolation to optimize the frequency response curve, establish a multi-dimensional inferiority fusion mathematical model, and finally make a judgment based on the frequency response statistical parameters. The patent of this invention shows higher sensitivity and reliability in diagnosing winding deformation faults, which is of great significance for extending the service life of transformers and improving the reliability and economy of power grid operation.

[0083] See also Figure 3 , Figure 3 FIG. 1 is a structural block diagram of an off-line monitoring device for transformer winding status in an embodiment of the present invention. Figure 3 The device shown comprises:

[0084] Data acquisition module 301: used to acquire offline frequency response test data of the transformer, wherein the offline frequency response test data at least includes a smooth frequency response curve;

[0085] Parameter determination module 302: used to obtain the resonant frequency and the resonant peak according to the smooth frequency response curve, and calculate the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency;

[0086] The first index module 303 is used to obtain a first comprehensive index at the resonant frequency by using the first transformer capacity, the first no-load loss, the first short-circuit loss and a preset multi-dimensional inferiority fusion model, wherein the first comprehensive index is used to reflect the change in the transformer winding performance;

[0087] The state determination module 304 is used to determine the current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index, where the second comprehensive index is obtained when the transformer is in normal operating state.

[0088] In a feasible implementation, the simulation test module is used to perform a short-circuit test and a no-load test on the transformer in a normal operating state to obtain a second no-load loss and a second short-circuit loss in a normal operating state;

[0089] The second index module is used to obtain the second comprehensive index according to the second no-load loss, the second short-circuit loss, the rated capacity of the transformer and the multi-dimensional inferiority fusion model.

[0090] It should be noted that if Figure 3 The contents of each module in the device shown are Figure 1 The contents of each step in the method shown are similar, so they are not described here to avoid repetition. For details, please refer to Figure 1 The content of each step in the method shown.

[0091] The present invention provides an offline monitoring device for transformer winding state, the device includes: a data acquisition module: used to acquire offline frequency response test data of the transformer, the offline frequency response test data at least includes a smooth frequency response curve; a parameter determination module: used to obtain the resonant frequency and the resonant peak according to the smooth frequency response curve, and calculate the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency; a first index module: used to obtain the first comprehensive index at the resonant frequency by using the first transformer capacity, the first no-load loss, the first short-circuit loss and the preset multi-dimensional inferiority fusion model, the first comprehensive index is used to reflect the change of transformer winding performance; a state determination module: used to determine the current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index, the second comprehensive index is obtained under the normal operating state of the transformer. Through the above device, higher sensitivity and reliability can be shown in diagnosing winding states such as winding deformation faults, which is of great significance for extending the service life of the transformer and improving the reliability and economy of power grid operation.

[0092] Figure 4 FIG. 1 shows an internal structure diagram of a computer device in an embodiment. The computer device may be a terminal or a server. Figure 4 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the above method. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the above method. Those skilled in the art can understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0093] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps: Figure 1 or Figure 2 Steps shown.

[0094] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor performs the following steps: Figure 1 or Figure 2 Steps shown.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0096] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for off-line monitoring of transformer winding status, characterized in that: The method comprises: Acquire offline frequency response test data of the transformer, wherein the offline frequency response test data at least includes a smooth frequency response curve; Obtaining the resonant frequency and the resonant peak value according to the smooth frequency response curve, and calculating the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency; Using the first transformer capacity, the first no-load loss, the first short-circuit loss and a preset multi-dimensional inferiority fusion model, a first comprehensive index at the resonant frequency is obtained, where the first comprehensive index is used to reflect the change in the transformer winding performance; The current winding state of the transformer is determined according to the first comprehensive index and a predetermined second comprehensive index, wherein the second comprehensive index is obtained when the transformer is in a normal operating state.

2. The method according to claim 1, characterized in that: The method further comprises: Performing a short-circuit test and a no-load test on the transformer in a normal operating state to obtain a second no-load loss and a second short-circuit loss in a normal operating state; The second comprehensive index is obtained according to the second no-load loss, the second short-circuit loss, the rated capacity of the transformer and the multi-dimensional inferiority fusion model.

3. The method according to claim 1 or 2, characterized in that: Determining the current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index includes: If the first comprehensive index is less than or equal to the second comprehensive index, determining that the current winding state of the transformer is a normal working state; If the first comprehensive index is greater than the second comprehensive index, it is determined that the current winding state of the transformer is an abnormal working state.

4. The method according to claim 1, characterized in that: The step of obtaining offline frequency response test data of the transformer includes: Sending an output signal instruction to the sinusoidal signal source, wherein the output signal instruction is used to instruct the sinusoidal signal source to send a sinusoidal wave excitation signal and continuously change the frequency of the sinusoidal wave excitation signal, wherein the sinusoidal wave excitation signal is injected from the end screen of the bushing on the low-voltage side of the transformer; The sine wave excitation signal is collected to obtain an injection signal at each frequency point; the output signal at each frequency point generated by the high-voltage side bushing of the transformer and after passing through the Rogowski coil, the common emitter amplifier circuit, and the Kalman filter is collected to obtain a response signal; Using the injection signal and the response signal at each frequency point, a discrete frequency response curve of the transformer is drawn; A piecewise cubic polynomial function is constructed using cubic spline interpolation to approximate a set of discrete data points, thereby transforming the discrete frequency response curve into a smooth frequency response curve.

5. The method according to claim 4, characterized in that: The discrete frequency response curve includes the following mathematical expression: Wherein, A is the amplitude frequency response curve gain of the discrete frequency response curve, V1 is the amplitude of the response signal after normalization processing, and V2 is the amplitude of the injection signal after normalization processing.

6. The method according to claim 1, characterized in that: The multi-dimensional inferiority fusion model includes the following mathematical expressions: Among them, ω1, ω2, ω3 are adjustment coefficients, I is the comprehensive index, Sn is the transformer capacity, Po is the no-load loss, and Pk is the short-circuit loss.

7. An off-line monitoring device for transformer winding status, characterized in that: The device comprises: Data acquisition module: used to acquire offline frequency response test data of the transformer, wherein the offline frequency response test data at least includes a smooth frequency response curve; Parameter determination module: used for obtaining the resonant frequency and the resonant peak value according to the smooth frequency response curve, and calculating the first transformer capacity, the first no-load loss and the first short-circuit loss at the resonant frequency; A first index module: used to obtain a first comprehensive index at the resonant frequency by using the first transformer capacity, the first no-load loss, the first short-circuit loss and a preset multi-dimensional inferiority fusion model, wherein the first comprehensive index is used to reflect the change in the transformer winding performance; A state determination module is used to determine the current winding state of the transformer according to the first comprehensive index and a predetermined second comprehensive index, wherein the second comprehensive index is obtained when the transformer is in normal operating state.

8. The device according to claim 7, characterized in that: The device also includes: Simulation test module: used to perform short-circuit test and no-load test on the transformer in normal operating state, and obtain the second no-load loss and the second short-circuit loss in normal operating state; The second index module is used to obtain the second comprehensive index according to the second no-load loss, the second short-circuit loss, the rated capacity of the transformer and the multi-dimensional inferiority fusion model.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 6.

10. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 6.

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