Transformer winding short circuit fault detection method and system, storage medium and equipment

Abstract

The embodiment of the invention discloses a transformer winding short circuit fault detection method. The method comprises the following steps: acquiring magnetic flux leakage data between transformer windings; determining a short-circuit fault area according to the magnetic flux leakage data; constructing a finite element model of the transformer; simulating the finite element model of the transformer based on the short-circuit fault area to obtain a short-circuit database; and acquiring magnetic flux leakage data between the transformer windings in real time, and determining the short-circuit fault position of the transformer windings according to the short-circuit database. According to the method, accurate detection of the short-circuit fault of the transformer winding is achieved by obtaining the magnetic flux leakage data, determining the short-circuit fault area, constructing the finite element model, obtaining the short-circuit database through simulation and locating the fault in real time, the accuracy and efficiency of fault detection are improved, and powerful guarantee is provided for safe operation of the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer winding detection, and in particular to a transformer winding short-circuit fault detection method, system, storage medium and equipment. Background Art

[0002] Transformers are an indispensable and important equipment in the power system, responsible for the transmission and distribution of electric energy; however, during long-term operation, transformers may be affected by various factors, such as overvoltage, overcurrent, environmental factors and equipment aging, which may lead to various faults.

[0003] Short-circuit fault is a common and serious type of fault. It can cause overheating of the internal winding of the transformer, leading to serious consequences such as damage to the insulation material, fire and explosion, and have a serious impact on the safe and stable operation of the power system.

[0004] At present, the diagnostic methods for transformer short-circuit faults mainly include manual inspection and local monitoring devices. The manual inspection method relies on the experience and skills of the inspectors, and has problems such as strong subjectivity, low efficiency, and easy misjudgment or omission. Although the local monitoring device can monitor the transformer status in a specific area, it cannot fully cover all areas of the transformer, is prone to omission or misdetection, cannot accurately determine the fault location, and cannot meet the needs of rapid diagnosis. Summary of the invention

[0005] Based on this, it is necessary to propose a transformer winding short-circuit fault detection method to address the above problems.

[0006] A transformer winding short-circuit fault detection method, the method comprising the following steps:

[0007] Obtain leakage magnetic data between transformer windings;

[0008] Determine the short circuit fault area according to the magnetic leakage data;

[0009] Construct transformer finite element model;

[0010] Simulating the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database;

[0011] The magnetic leakage data between transformer windings is collected in real time and the short-circuit fault position of the transformer winding is determined according to the short-circuit database.

[0012] In the above solution, the step of obtaining the magnetic leakage data between transformer windings specifically includes:

[0013] A plurality of magnetic sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group;

[0014] The leakage magnetic flux at all positions between the windings is measured according to the sensor group to obtain the leakage magnetic flux data between the transformer windings.

[0015] In the above solution, after obtaining the magnetic leakage data between the transformer windings, the method further includes:

[0016] The collected magnetic flux leakage data is preprocessed, and the preprocessing step includes denoising, filtering, calibration and standardization.

[0017] In the above solution, after obtaining the magnetic leakage data between the transformer windings, the method further includes:

[0018] The pre-processed leakage magnetic data is subjected to pattern recognition to determine the operating state of the transformer, wherein the operating state of the transformer includes a normal and stable operating state, a closing state, and a winding short-circuit state.

[0019] In the above solution, determining the short circuit fault area according to the magnetic leakage data specifically includes:

[0020] When it is determined that the operating state of the transformer is in a winding short-circuit state, the pre-processed leakage magnetic data are arranged according to the change amplitude to obtain a leakage magnetic change sequence table;

[0021] The short circuit fault area is determined according to the leakage magnetic field change sequence table.

[0022] In the above solution, simulating the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database specifically includes:

[0023] Setting short-circuit conditions for the short-circuit position within the short-circuit fault area and conducting transient research thereon;

[0024] Obtaining a leakage flux traversal result of the transformer finite element model, wherein the leakage flux traversal result includes a port current waveform, a voltage waveform, and winding leakage flux data;

[0025] A short circuit database is constructed according to the port current waveform, voltage waveform and winding leakage flux data.

[0026] In the above scheme, the real-time acquisition of the leakage magnetic data between the transformer windings and the determination of the short-circuit fault position of the transformer winding according to the short-circuit database specifically include:

[0027] Comparing the magnetic flux leakage data of the sensor with the magnetic flux leakage traversal result;

[0028] Determine the corresponding fault condition according to the minimum difference;

[0029] The grid-side current and voltage recording data during the short-circuit fault are compared with the current and voltage data obtained from the finite element model to determine the short-circuit fault point.

[0030] The present application also includes a transformer winding short-circuit fault detection system, the system comprising: a magnetic leakage data acquisition unit, a short-circuit fault determination unit, a simulation model construction unit and a fault location determination unit;

[0031] The magnetic flux leakage data acquisition unit is used to acquire magnetic flux leakage data between transformer windings;

[0032] The short-circuit fault determination unit is used to determine the short-circuit fault area according to the magnetic leakage data;

[0033] The simulation model building unit is used to build a transformer finite element model; based on the short-circuit fault area, the transformer finite element model is simulated to obtain a short-circuit database;

[0034] The fault location determination unit is used to collect leakage magnetic data between transformer windings in real time and determine the short-circuit fault location of the transformer winding according to the short-circuit database.

[0035] The present application also includes a readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps:

[0036] Obtain leakage magnetic data between transformer windings;

[0037] Determine the short circuit fault area according to the magnetic leakage data;

[0038] Construct transformer finite element model;

[0039] Simulating the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database;

[0040] The magnetic leakage data between transformer windings is collected in real time and the short-circuit fault position of the transformer winding is determined according to the short-circuit database.

[0041] The present application also includes a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to perform the following steps:

[0042] Obtain leakage magnetic data between transformer windings;

[0043] Determine the short circuit fault area according to the magnetic leakage data;

[0044] Construct transformer finite element model;

[0045] Simulating the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database;

[0046] The magnetic leakage data between transformer windings is collected in real time and the short-circuit fault position of the transformer winding is determined according to the short-circuit database.

[0047] The embodiment of the present invention has the following beneficial effects: firstly obtain the leakage magnetic data between the transformer windings; determine the short-circuit fault area according to the leakage magnetic data; construct a transformer finite element model; simulate the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database; collect the leakage magnetic data between the transformer windings in real time and determine the short-circuit fault position of the transformer winding according to the short-circuit database. The present invention realizes the accurate detection of transformer winding short-circuit faults by obtaining leakage magnetic data, determining the short-circuit fault area, constructing a finite element model, simulating to obtain a short-circuit database, and locating the fault in real time. This method not only improves the accuracy and efficiency of fault detection, but also provides a strong guarantee for the safe operation of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] 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.

[0049] in:

[0050] Figure 1 The figure is a flow chart of a transformer winding short-circuit fault detection method in one embodiment. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] In the following description, a large number of specific details are given in order to provide a more thorough understanding of the present invention; however, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details; in other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described, and it should be understood that the present invention can be implemented in different forms and should not be interpreted as limited to the embodiments presented here; on the contrary, providing these embodiments will make the disclosure thorough and complete and fully convey the scope of the present invention to those skilled in the art.

[0053] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present invention. When used herein, the singular forms "a", "an" and "said / the" are also intended to include the plural forms, unless the context clearly indicates otherwise, and it should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0054] To facilitate understanding, the relevant terms involved in this application are first introduced below.

[0055] (1) Leakage magnetic data refers to the magnetic flux data that cannot completely pass through the core due to magnetic circuit saturation, winding structure defects or external factors during the operation of the transformer, but leaks out from around the core. This data can be collected by magnetic sensors installed around the transformer windings.

[0056] (2) Transformer winding is one of the most core components of the transformer, which is responsible for the transmission and conversion of electrical energy. The winding is usually made of copper wire or aluminum wire and insulated in transformer oil or other insulating materials;

[0057] (3) Transient research refers to the study of the state changes that a system undergoes in a short period of time, focusing on the dynamic response process of the system after being subjected to transient disturbances or excitations.

[0058] In order to thoroughly understand the present invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by the present invention; optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementation methods.

[0059] like Figure 1As shown, in one embodiment, a transformer winding short-circuit fault detection method is provided, and the transformer winding short-circuit fault detection method includes steps S101 to S105, which are described in detail as follows:

[0060] S101, obtaining magnetic flux leakage data between transformer windings;

[0061] This step is the starting point of the detection method. Through the magnetic sensor group, the size and direction of the leakage flux between the transformer windings can be measured. The size and direction of the leakage flux are related to factors such as the current, number of turns, and geometric shape of the winding; therefore, it can reflect the health status of the winding, provide basic data for subsequent fault diagnosis, and provide a basis for determining the fault area and establishing a simulation model.

[0062] In some embodiments, obtaining leakage magnetic data between transformer windings specifically includes:

[0063] A plurality of magnetic sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group;

[0064] The leakage flux at all positions between the windings is measured according to the sensor group, and the leakage flux data between the transformer windings is obtained.

[0065] Preferably, the position of a single sensor is designed according to the expected accuracy of the leakage flux measurement, so that a single sensor is responsible for the leakage flux monitoring of the corresponding coil, and multiple sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group that can correctly measure the leakage flux at all positions between the windings.

[0066] Furthermore, non-magnetic brackets or clamps are used to fix the sensors to ensure their stable position during operation and prevent distortion of the measurement data due to vibration or other external factors.

[0067] In some embodiments, after obtaining the magnetic flux leakage data between transformer windings, the method further includes:

[0068] The collected magnetic flux leakage data is preprocessed, and the preprocessing steps include denoising, filtering, calibration and standardization.

[0069] During the operation of the transformer, the data acquisition system is started to record the leakage magnetic data obtained from each sensor in real time, and the collected leakage magnetic data is preprocessed as necessary to improve the reliability and accuracy of the data.

[0070] In some embodiments, after obtaining the magnetic flux leakage data between transformer windings, the method further includes:

[0071] The pre-processed leakage magnetic data is subjected to pattern recognition to determine the operating state of the transformer, which includes the normal and stable operating state, the closing state, and the winding short-circuit state.

[0072] Specifically, magnetic flux leakage data can be divided into three modes:

[0073] (1) When the transformer is operating normally and stably, the effective value of leakage flux remains basically unchanged;

[0074] (2) When the transformer is in the closed state, all sensor data change significantly;

[0075] (3) When a short circuit occurs in the transformer winding, the leakage magnetic flux data near the short circuit winding changes significantly, while the leakage magnetic flux changes at other locations are not obvious.

[0076] By analyzing the changing trend and pattern of the leakage magnetic data, the operating status of the transformer can be judged. When a short circuit occurs in the winding, the leakage magnetic data will show the pattern (3).

[0077] S102, determining a short circuit fault area according to magnetic leakage data;

[0078] By analyzing the leakage magnetic data, areas with abnormal leakage magnetic flux (such as changes in intensity, distribution, etc.) can be identified, so as to preliminarily determine the area where the short-circuit fault may occur. For example, a short-circuit fault will cause the leakage magnetic flux in this area to increase, narrowing the fault diagnosis range, improving diagnostic efficiency, avoiding unnecessary inspections of the entire transformer, and improving the efficiency and accuracy of subsequent steps.

[0079] In some embodiments, determining the short circuit fault area according to the magnetic leakage data specifically includes:

[0080] When it is determined that the transformer is in a winding short-circuit state, the pre-processed leakage magnetic data are arranged according to the change amplitude to obtain a leakage magnetic change sequence table;

[0081] Determine the short-circuit fault area based on the leakage magnetic change sequence table.

[0082] S103, constructing a transformer finite element model;

[0083] According to the physical structure and electrical parameters of the transformer, an accurate finite element model is constructed. The finite element model can simulate the magnetic field distribution of the transformer winding, so as to calculate the leakage flux distribution under different fault conditions, providing a basis for subsequent simulation and establishment of a short-circuit database.

[0084] Specifically, a multi-segment winding finite element model suitable for transformer winding short-circuit fault analysis is established in COMSOL software based on actual transformer parameters. The specific steps are as follows: the transformer finite element model is divided into three parts: iron core, winding and oil tank, where the iron core structure is designed in the form of stacked silicon steel sheets; the winding is divided into multiple cake units according to the actual wire cake structure, and the cake unit is constructed according to a single-conductor spiral structure; the oil tank is simplified to a rectangular structure.

[0085] S104, simulating the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database;

[0086] By simulating the finite element model, the leakage magnetic data under different short-circuit fault locations can be obtained and a short-circuit database can be established. The database contains the leakage magnetic characteristics under different fault states, and a corresponding relationship between the short-circuit fault and the leakage magnetic data is established, which can be used for subsequent fault diagnosis and provide a reference for actual fault diagnosis. By comparing the leakage magnetic data collected in real time with the database data, the database data that best matches the actual fault situation can be identified, thereby determining the fault location.

[0087] In some embodiments, simulating a transformer finite element model based on a short-circuit fault area to obtain a short-circuit database specifically includes:

[0088] Set short-circuit conditions at the short-circuit locations within the short-circuit fault area and conduct transient studies on them;

[0089] Obtain the leakage flux traversal results of the transformer finite element model, which include the port current waveform, voltage waveform and winding leakage flux data;

[0090] A short-circuit database is constructed based on the port current waveform, voltage waveform and winding leakage flux data.

[0091] S105, collecting leakage magnetic data between transformer windings in real time and determining the short-circuit fault position of the transformer winding according to the short-circuit database.

[0092] During the operation of the transformer, the leakage magnetic data between the windings is collected in real time, and the real-time collected leakage magnetic data is compared and analyzed with the data in the short-circuit database to determine the specific location of the short-circuit fault, thereby achieving real-time and accurate detection of transformer winding short-circuit faults and improving the timeliness and effectiveness of fault handling.

[0093] By comparing the real-time collected magnetic leakage data with the short-circuit database data, a matching index, such as distance or similarity, can be calculated. The fault location corresponding to the database data with the highest matching degree is the most likely fault location.

[0094] In some embodiments, real-time collection of magnetic flux leakage data between transformer windings and determination of the short-circuit fault location of the transformer winding according to a short-circuit database specifically includes:

[0095] Compare the sensor's magnetic flux leakage data with the magnetic flux leakage traversal results;

[0096] Determine the corresponding fault condition according to the minimum difference;

[0097] The grid-side current and voltage recording data during the short-circuit fault are compared with the current and voltage data obtained from the finite element model to determine the short-circuit fault point.

[0098] Specifically, the port current data, voltage data and magnetic flux leakage data are used as the basis for judgment. The port current and voltage data can be obtained through fault recording, and the magnetic flux leakage data is provided by the magnetic sensor group.

[0099] First, based on the traversal results obtained by simulation, the leakage magnetic data of the sensor is compared with the leakage magnetic traversal results of the finite element model, and the fault condition with the smallest difference is selected for the next judgment. Furthermore, the grid-side current and voltage recording data during the short-circuit fault are compared with the current and voltage data obtained by the finite element model. If the waveforms of the two have a high degree of consistency, the short-circuit point is considered to be the correct fault point; otherwise, continue to compare the data of other fault points in the area until the short-circuit fault point is determined.

[0100] In summary, the present invention utilizes the relationship between electromagnetic coupling and adopts a magnetic sensor group to monitor the leakage magnetic data in real time, comprehensively covers all areas of the transformer, and monitors the possible locations of short-circuit fault points. It can not only accurately determine the short-circuit location, but also avoid the uncertainty of subjective judgment in traditional methods, which is of great significance to the safe operation and fault analysis of the transformer.

[0101] The present application also includes a transformer winding short-circuit fault detection system, the system comprising: a magnetic leakage data acquisition unit, a short-circuit fault determination unit, a simulation model construction unit and a fault location determination unit;

[0102] A magnetic flux leakage data acquisition unit, used for acquiring magnetic flux leakage data between transformer windings;

[0103] A short-circuit fault determination unit, used to determine a short-circuit fault area according to magnetic leakage data;

[0104] A simulation model building unit is used to build a transformer finite element model; the transformer finite element model is simulated based on the short-circuit fault area to obtain a short-circuit database;

[0105] The fault location determination unit is used to collect the leakage magnetic data between the transformer windings in real time and determine the short-circuit fault location of the transformer winding according to the short-circuit database.

[0106] The present application also includes a readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the following steps:

[0107] Obtain leakage magnetic data between transformer windings;

[0108] Determine the short circuit fault area based on the magnetic flux leakage data;

[0109] Construct transformer finite element model;

[0110] Simulate the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database;

[0111] The leakage magnetic data between transformer windings is collected in real time and the short-circuit fault location of the transformer winding is determined based on the short-circuit database.

[0112] The present application also includes a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to perform the following steps:

[0113] Obtain leakage magnetic data between transformer windings;

[0114] Determine the short circuit fault area based on the magnetic flux leakage data;

[0115] Construct transformer finite element model;

[0116] Simulate the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database;

[0117] The leakage magnetic data between transformer windings is collected in real time and the short-circuit fault location of the transformer winding is determined based on the short-circuit database.

[0118] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments 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 the present application may 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 various 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).

[0119] The technical features of the above embodiments can 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.

[0120] 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 patent of the present application. It should be pointed out that, for ordinary technicians in this field, several modifications 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. The above disclosure is only a preferred embodiment of the present invention, and of course it cannot be used to limit the scope of the rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope covered by the present invention.

Claims

1. A transformer winding short-circuit fault detection method, characterized in that: The method comprises: Obtain leakage magnetic data between transformer windings; Determine the short circuit fault area according to the magnetic leakage data; Construct transformer finite element model; Simulating the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database; The magnetic leakage data between transformer windings is collected in real time and the short-circuit fault position of the transformer winding is determined according to the short-circuit database.

2. A transformer winding short-circuit fault detection method according to claim 1, characterized in that: The obtaining of magnetic leakage data between transformer windings specifically includes: A plurality of magnetic sensors are evenly arranged along the axial direction of the transformer winding to form a sensor group; The leakage magnetic flux at all positions between the windings is measured according to the sensor group to obtain the leakage magnetic flux data between the transformer windings.

3. A transformer winding short-circuit fault detection method according to claim 1, characterized in that: After obtaining the leakage magnetic data between transformer windings, the method further includes: The collected magnetic flux leakage data is preprocessed, and the preprocessing step includes denoising, filtering, calibration and standardization.

4. A transformer winding short-circuit fault detection method according to claim 3, characterized in that: After obtaining the leakage magnetic data between transformer windings, the method further includes: The pre-processed leakage magnetic data is subjected to pattern recognition to determine the operating state of the transformer, wherein the operating state of the transformer includes a normal and stable operating state, a closing state, and a winding short-circuit state.

5. A transformer winding short-circuit fault detection method according to claim 4, characterized in that: Determining the short circuit fault area according to the magnetic leakage data specifically includes: When it is determined that the operating state of the transformer is in a winding short-circuit state, the pre-processed leakage magnetic data are arranged according to the change amplitude to obtain a leakage magnetic change sequence table; The short circuit fault area is determined according to the leakage magnetic field change sequence table.

6. A transformer winding short-circuit fault detection method according to claim 5, characterized in that: The simulating the transformer finite element model based on the short-circuit fault area to obtain a short-circuit database specifically includes: Setting short-circuit conditions for the short-circuit position within the short-circuit fault area and conducting transient research thereon; Obtaining a leakage flux traversal result of the transformer finite element model, wherein the leakage flux traversal result includes a port current waveform, a voltage waveform, and winding leakage flux data; A short circuit database is constructed according to the port current waveform, voltage waveform and winding leakage flux data.

7. A transformer winding short-circuit fault detection method according to claim 1, characterized in that: The real-time collection of magnetic leakage data between transformer windings and determining the short-circuit fault position of the transformer winding according to the short-circuit database specifically includes: Comparing the magnetic flux leakage data of the sensor with the magnetic flux leakage traversal result; Determine the corresponding fault condition according to the minimum difference; The grid-side current and voltage recording data during the short-circuit fault are compared with the current and voltage data obtained from the finite element model to determine the short-circuit fault point.

8. A transformer winding short-circuit fault detection system, characterized in that: The system comprises: a magnetic flux leakage data acquisition unit, a short circuit fault determination unit, a simulation model construction unit and a fault position determination unit; The magnetic flux leakage data acquisition unit is used to acquire magnetic flux leakage data between transformer windings; The short-circuit fault determination unit is used to determine the short-circuit fault area according to the magnetic leakage data; The simulation model building unit is used to build a transformer finite element model; based on the short-circuit fault area, the transformer finite element model is simulated to obtain a short-circuit database; The fault location determination unit is used to collect leakage magnetic data between transformer windings in real time and determine the short-circuit fault location of the transformer winding according to the short-circuit database.

9. A readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 7.

10. A computer device 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 executes the steps of the method according to any one of claims 1 to 7.

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