KR102118340B1 - Transformer fault diagnosis with sound information - Google Patents

Abstract

Disclosed is a method for diagnosing transformer failure using sound information. The transformer fault diagnosis method includes: detecting a sound section of a transformer from sound data collected by a power transformer; Removing harmonics from the transformer sound section; Converting the transformer sound from which the harmonics have been removed into a sound intensity level; And diagnosing the failure of the power transformer using the sound intensity.

Description

Transformer failure diagnosis method using sound information{TRANSFORMER FAULT DIAGNOSIS WITH SOUND INFORMATION}

The description below relates to a technique for diagnosing the failure of a power transformer.

Power transformers are very susceptible to failures as they cause power outages or human damage in the event of a failure. Typical causes of accidents in power transformers include short-circuiting and short-circuiting of the windings, mixing of high-voltage windings, and failure of the ULTC (no-load automatic tap-changer). In particular, when aging or a maximum power load occurs, that is, if an overload condition is continuously generated, the insulation of the power transformer is destroyed, resulting in accidents such as burnout, explosion, and fire.

It is very important to predict the abnormality of the power transformer to prevent such an accident. Conventionally, a method of predicting a life by determining the degree of deterioration of a power transformer and a method of monitoring load by calculating data such as voltage and current information and load power amount (kWh) are used.

As an example, Korean Patent Publication No. 10-2016-0020657 (power transformer life prediction system and method) measures the obtained data with a current transformer to determine the degree of deterioration of the power transformer. It relates to a method of calculating an accelerated deterioration index by using and analyzing a state according to the degree of deterioration. It aims to provide a technique for predicting the life of a power transformer due to the external temperature of the transformer.

In addition, Korean Patent Publication No. 10-2012-0062038 (Transformer Smart Load Monitoring System and Method) shows the current status of customers such as the number of incoming load number, customer number, and meter reading date for each load that can be updated from each transformer for a number of customers. It relates to a remote monitoring method for calculating the loss rate through the meter reading power and load power using data and a plurality of current and voltage data. It aims to provide a technique for monitoring the condition of a low voltage load due to the loss rate of the transformer.

The current fault diagnosis manual of a power transformer is to stop the transformer immediately when a noise occurs compared to a normal transformer sound through a method in which a person directly listens to the sound of the transformer.

Provided is a method and system for diagnosing the failure of a power transformer using sound.

A method for diagnosing a transformer failure performed in a computer device, the method comprising: detecting, by at least one processor, a transformer sound section from sound data collected by a power transformer; Removing harmonics from the transformer sound section by the at least one processor; Converting, by the at least one processor, the harmonic removed transformer sound to a sound intensity level; And diagnosing the failure of the power transformer by using the sound intensity by the at least one processor, wherein the detecting comprises: a smooth filter that is a low pass filter. Removing white noise from the sound data using; And classifying the transformer sound section from the sound data from which the white noise has been removed by applying a support vector data description (SVDD), wherein the classifying step includes learning the actual sound of a transformer that does not contain noise. Applying the SVDD model as a sliding window processing method to classify the transformer sound section from the sound data from which the white noise has been removed, and to apply the SVDD in sound streaming, the sliding window is processed to have variable parameters in length. Detecting the sound section of the transformer by applying a method, and the diagnosing step, compares the average, maximum, and minimum statistical values of the sound intensity with the predetermined average, maximum, and minimum statistical values of the normal transformer sound intensity. To provide a method for diagnosing a transformer failure, characterized in that it determines whether the power transformer has failed.

According to one aspect, the detecting may include collecting the sound data wirelessly through the power transformer or a microphone sensor attached to the periphery of the power transformer.

According to another aspect, the detecting may further include verifying the transformer sound section classified through the SVDD using a convolutional neural network (CNN).

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According to another aspect, in the removing step, a harmonic may be removed from the sound section of the transformer by using a low pass filter.

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A computer program stored in a computer-readable recording medium to execute a transformer failure diagnosis method, the transformer failure diagnosis method comprising: detecting a transformer sound section from sound data collected by a power transformer; Removing harmonics from the transformer sound section; Converting the transformer sound from which the harmonics have been removed into sound intensity levels; And diagnosing the failure of the power transformer by using the sound intensity, wherein the detecting comprises using a low pass filter, a smooth filter, to white the sound data. Removing noise; And classifying the transformer sound section from the sound data from which the white noise has been removed by applying a support vector data description (SVDD), wherein the classifying step includes learning the actual sound of a transformer that does not contain noise. Applying the SVDD model as a sliding window processing method to classify the transformer sound section from the sound data from which the white noise has been removed, and to apply the SVDD in sound streaming, the sliding window is processed to have variable parameters in length. Detecting the sound section of the transformer by applying a method, and the diagnosing step, comparing the average, maximum, and minimum statistical values of the sound intensity with the predetermined average, maximum, and minimum statistical values of the normal transformer sound intensity It provides a computer program stored in a computer-readable recording medium, characterized in that to determine whether the power transformer has failed.

A computer implemented transformer fault diagnosis system, comprising: at least one processor configured to execute computer readable instructions contained in a memory, the at least one processor comprising: a transformer sound section from sound data collected by a power transformer Sound detection unit for detecting; A harmonic removing unit that removes harmonics from the transformer sound section; An intensity converter for converting the transformer sound from which the harmonics have been removed into sound intensity levels; And a transformer diagnosis unit for diagnosing the failure of the power transformer using the sound intensity, and the sound detection unit uses a low pass filter, a smooth filter, to generate white color from the sound data. Remove the noise, apply the support vector data description (SVDD) to classify the transformer sound section from the noise data from which the white noise has been removed, and slide the SVDD model where the actual sound of the transformer that does not contain noise is learned through a sliding window ( sliding window) to classify the transformer sound section from the white noise-removed sound data, and apply the sliding window processing method so that the variable has a variable length in order to apply the SVDD in sound streaming. Failure of the power transformer by detecting a sound section and comparing the average, maximum, and minimum statistical values of the sound intensity with the predetermined average, maximum, and minimum statistical values of the sound transformer of the normal transformer It provides a transformer fault diagnosis system characterized in that to determine whether.

According to embodiments of the present invention, after removing the harmonics from the sound data of the power transformer, it is transformed into a sound intensity level (SIL) to determine whether the power transformer has failed based on this, thereby deteriorating the power transformer. The fault condition can be effectively managed, and the performance of diagnosing transformer faults using sound can be further improved.

1 is a block diagram showing an example of a computer device according to an embodiment of the present invention.
2 is a diagram illustrating an example of components that may be included in a processor of a computer device according to an embodiment of the present invention.
3 is a flowchart illustrating an example of a transformer fault diagnosis method that can be performed by a computer device according to an embodiment of the present invention.
4 is an exemplary diagram for explaining a process of collecting sound data of a power transformer in an embodiment of the present invention.
5 is an exemplary diagram for explaining a process of detecting a sound section of a transformer in an embodiment of the present invention.
6 is an exemplary view for explaining a process of removing harmonics in a transformer sound section in an embodiment of the present invention.
7 to 9 are exemplary diagrams for explaining a process of converting a transformer sound into sound intensity in an embodiment of the present invention.
10 is an exemplary diagram for explaining a process of diagnosing a transformer failure using sound intensity in an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The present invention relates to a technique for diagnosing whether or not a power transformer has failed, and more particularly, to a technique for diagnosing and managing a failure using a transformer sound.

1 is a block diagram showing an example of a computer device according to an embodiment of the present invention.

A transformer fault diagnosis system according to embodiments of the present invention may be implemented through the computer device 100 of FIG. 1. For example, a computer program according to an embodiment may be installed and driven in the computer device 100, and the computer device 100 may diagnose a transformer failure according to embodiments of the present invention under the control of a driven computer program. The method can be performed.

The transformer fault diagnosis system may be configured as a PC-based program or an application for mobile terminals. The transformer fault diagnosis system in this embodiment may be implemented as a program that operates independently, or may be implemented in an in-app form of a specific application to operate on the specific application.

The transformer fault diagnosis system through the computer device 100 may be a fixed terminal or a mobile terminal. For example, the computer device 100 may be a device such as a computer (PC), a smart phone, a tablet, or a laptop.

As shown in FIG. 1, the computer device 100 may include a memory 110, a processor 120, a communication interface 130, and an input/output interface 140 as components for executing a transformer failure diagnosis method. have.

The memory 110 is a computer-readable recording medium, and may include a non-permanent mass storage device such as random access memory (RAM), read only memory (ROM), and a disk drive. Here, a non-destructive large-capacity recording device such as a ROM and a disk drive may be included in the computer device 100 as a separate permanent storage device separate from the memory 110. In addition, an operating system and at least one program code may be stored in the memory 110. These software components may be loaded into the memory 110 from a computer-readable recording medium separate from the memory 110. Such a separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, disk, tape, DVD/CD-ROM drive, and memory card. In other embodiments, software components may be loaded into memory 110 through communication interface 130 rather than a computer-readable recording medium. For example, software components may be loaded into memory 110 of computer device 100 based on a computer program installed by files received over network 160.

The processor 120 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processor 120 by memory 110 or communication interface 130. For example, the processor 120 may be configured to execute instructions received according to program codes stored in a recording device such as the memory 110.

The communication interface 130 may provide a function for the computer device 100 to communicate with other computer devices (not shown) through the network 160. For example, a request, command, data, file, etc. generated by the processor 120 of the computer device 100 according to program code stored in a recording device such as the memory 110 is controlled by the communication interface 130 through the network ( 160) to another computer device. Conversely, signals, commands, data, files, and the like from other computer devices may be received through the network 160 to the computer device 100 through the communication interface 130 of the computer device 100. Signals, commands, data, and the like received through the communication interface 130 may be transferred to the processor 120 or the memory 110, and a file, etc., may be a storage medium (described above) that the computer device 100 may further include. Permanent storage device).

The input/output interface 140 may be a means for interfacing with the input/output device 150. For example, the input device may include a device such as a microphone, keyboard or mouse, and the output device may include a device such as a display or speaker. As another example, the input/output interface 140 may be a means for interfacing with a device in which functions for input and output are integrated into one, such as a touch screen. The input/output device 150 may be configured as a computer device 100 and a single device.

Also, in other embodiments, the computer device 100 may include more components than those in FIG. 1. However, there is no need to clearly show most prior art components. For example, the computer device 100 is implemented to include at least some of the input/output devices connected to the input/output interface 140 described above, or further includes other components such as a transceiver, camera, various sensors, and databases. It may include.

2 is a diagram illustrating an example of components that a processor of a computer device according to an embodiment of the present invention may include, and FIG. 3 is a transformer failure that can be performed by the computer device according to an embodiment of the present invention It is a flowchart showing an example of a diagnostic method.

As shown in FIG. 2, the processor 120 may include a data collection unit 201, a sound detection unit 202, a harmonic removal unit 203, an intensity conversion unit 204, and a transformer diagnosis unit 205. Can be. The components of the processor 120 may be expressions of different functions performed by the processor 120 according to control instructions provided by at least one program code. For example, the data collection unit 201 may be used as a functional expression to operate the processor 120 to control the computer device 100 to collect sound data.

The processor 120 and the components of the processor 120 may perform steps S301 to S305 included in the transformer fault diagnosis method of FIG. 3. For example, the processor 120 and components of the processor 120 may be implemented to execute instructions according to at least one program code and the code of the operating system included in the memory 320. Here, at least one program code may correspond to a code of a program implemented to process a transformer fault diagnosis method.

The transformer fault diagnosis method may not occur in the order shown, and some of the steps may be omitted or additional processes may be further included.

The processor 120 may load the program code stored in the program file for the transformer fault diagnosis method into the memory 320. For example, the program file for the transformer fault diagnosis method may be stored in the permanent storage device 330 described with reference to FIG. 3, and the processor 120 may be stored from the program file stored in the permanent storage device 330 through the bus. The computer device 100 may be controlled such that the program code is loaded into the memory 320. At this time, the processor 120 and the data collection unit 201 included in the processor 120, the sound detection unit 202, the harmonic removal unit 203, the intensity conversion unit 204, and the transformer diagnosis unit 205, respectively It may be different functional expressions of the processor 120 for executing a transformer fault diagnosis method by executing an instruction of a corresponding portion of the program code loaded in the memory 320. For the implementation of the transformer fault diagnosis method, the processor 120 and the components of the processor 120 may process an operation according to a direct control command or control the computer device 100.

The transformer fault diagnosis method belongs to the IoT fault diagnosis technology field, and includes artificial intelligence and pattern recognition technology to classify/detect sounds, and signal processing technology to convert transformer sound data into DB information.

In step S301, the data collection unit 201 may collect sound data of a power transformer using a sound detection sensor such as a microphone sensor. The sound detection sensor may be composed of components included in the transformer failure diagnosis system, or may be attached to a power transformer and use a sensor having a communication function to transmit sound data of the power transformer to a remote transformer failure diagnosis system. Things are also possible.

4 shows an example of a process (S301) for collecting sound data of a power transformer.

Referring to FIG. 4, the data collection unit 201 remotely receives sound data of the power transformer 400 through a power transformer 400 or a microphone sensor 401 attached to the periphery of the power transformer 400. Can be collected.

The sound data of the power transformer collected by the data collection unit 201 is a lossless, uncompressed wav file format, a sampling rate of 44,100 Hz, and a single channel format of Mono format. .

In the power transformer failure diagnosis technology, the abnormality of the state before the explosion due to the failure can be predicted using the sound of the power transformer.

However, the power transformer has a relatively small sound level, such as 60 Hz, and thus has a problem that it is difficult to analyze harmonics or recognize patterns. In the present invention, in order to solve such a problem, after removing harmonics from the transformer sound excluding noise, the sound intensity may be converted to diagnose a failure based on this.

In FIG. 3 again, in step S302, the sound detector 202 may detect the transformer sound from the sound data collected in step S301 using a machine learning method or the like.

5 shows an example of a process (S302) of detecting a transformer sound section excluding noise.

First, the sound detector 202 may remove white noise using a low pass filter, a smooth filter. The characteristic of transformer sound for fault diagnosis is diagnosed through amplitude, so a low-frequency filter that removes harmonic components can be used to remove white noise. In other words, white noise, which is a relatively small noise, can be removed using a flat filter in the signal processing field.

The sound detector 202 may classify the actual sound section and general noise of the transformer from the sound data from which the white noise has been removed. For example, the sound detector 202 may classify a transformer sound section and general noise by applying a support vector data description (SVDD) of the pattern recognition classification technique.

In order to classify the general noise, which is a relatively large noise, first, sound data of a transformer with no noise mixed in an enclosed space such as a noise room can be collected. And, by learning the collected sound data using SVDD, it is possible to create a learning model for the actual transformer sound. As SVDD is a binary classifier, only true and false can be discriminated, so it is possible to discriminate whether it is a transformer noise or a general noise rather than a transformer sound.

Since the current entering the transformer is mainly 60 Hz, in order to apply SVDD in a normal stream, the sound section of the transformer can be detected by applying a sliding window processing method so that the length has a variable parameter (for example, 0.1 to 1 second). .

Therefore, the sound detection unit 202 may detect the transformer sound section by applying the binary classifier SVDD by a sliding window processing method.

The sound detection unit 202 may verify whether a transformer sound section is a transformer sound through machine learning. The sound detector 202 may determine whether the transformer sound section corresponds to the sound of the transformer using a convolutional neural network (CNN) technique.

The sound detector 202 learns sound data of a transformer with no noise by applying a CNN technique, and verifies whether the sound is a transformer or a transformer by verifying a transformer sound section classified from SVDD through this learning model. .

Both SVDD and CNN are algorithms for classification, SVDD has excellent performance in classifying normal and abnormal groups, and CNN is a machine learning method applicable to complex situations.

Therefore, in the present invention, considering the characteristics of SVDD and CNN, SVDD is applied to detect a transformer sound section in sound streaming, and CNN is applied to verify the detected transformer sound section.

In FIG. 3 again, in step S303, the harmonic removal unit 203 may remove harmonics in the transformer sound section detected in step S302.

6 shows an example of a process (S303) for removing harmonics in the transformer sound section. As an example, the harmonic removing unit 203 may remove harmonics from the transformer sound section using a low frequency filter.

In FIG. 3 again, in step S304, the intensity converter 204 may convert the transformer sound from which the harmonics are removed in step S303 to sound intensity.

7 shows an example of a process (S304) of converting the sound of a transformer from which harmonics have been removed into sound intensity. In other words, the intensity converter 204 may measure the intensity of the transformer sound from which harmonics are removed through sound intensity conversion.

8 shows the sound waveform and sound intensity of a normal transformer, and FIG. 9 shows the sound waveform and sound intensity of a faulty transformer. 8 and 9, it can be seen that the sound strength of the faulty transformer is greater than that of the normal transformer in some sections.

In FIG. 3 again, in step S305, the transformer diagnosis unit 205 may diagnose whether the transformer has failed using the sound intensity converted in step S304. For example, the transformer diagnosis unit 205 may determine whether the transformer has failed by comparing the sound strength of the transformer with a predetermined sound strength.

10 illustrates an example of a process (S305) for diagnosing a transformer failure using sound intensity.

Referring to FIG. 10, the transformer diagnosis unit 205 may diagnose whether or not a failure occurs by comparing the normal transformer sound intensity and the abnormal transformer sound intensity. For example, the transformer diagnosis unit 205 considers the transformer sound or the noise of a transformer that does not mix noise collected in an enclosed space as the normal transformer sound during the first operation of the power transformer, and the sound signal waveform of the transformer to be diagnosed, Record the sound intensity, and the average, maximum, and minimum statistics, and check the transformer sound information periodically for a unit period (for example, 1 day), and compare the normal transformer with the average, maximum, and minimum statistics to determine whether there is a failure. Can grasp.

As described above, according to embodiments of the present invention, after removing harmonics from the sound data of the power transformer, it is transformed into sound intensity to determine whether the power transformer has failed based on this, thereby effectively managing the aging or failure state of the power transformer. Can improve the performance of the transformer fault diagnosis using sound.

The device described above may be implemented with hardware components, software components, and/or combinations of hardware components and software components. For example, the devices and components described in the embodiments may include a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor (micro signal processor), a microcomputer, a field programmable gate array (FPGA), or a programmable (PLU) It may be implemented using one or more general purpose computers or special purpose computers, such as a logic unit, microprocessor, or any other device capable of executing and responding to instructions. The processing device may perform an operating system (OS) and one or more software applications running on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of the software. For convenience of understanding, a processing device may be described as one being used, but a person having ordinary skill in the art, the processing device may include a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that may include. For example, the processing device may include a plurality of processors or a processor and a controller. In addition, other processing configurations, such as parallel processors, are possible.

The software may include a computer program, code, instruction, or a combination of one or more of these, and configure the processing device to operate as desired, or process independently or collectively You can command the device. Software and/or data may be embodied in any type of machine, component, physical device, computer storage medium, or device in order to be interpreted by the processing device or to provide instructions or data to the processing device. have. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. In this case, the medium may continuously store a program executable on a computer or may be temporarily stored for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or several hardware combinations, and is not limited to a medium directly connected to a computer system, but may be distributed on a network. Examples of the medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks, And program instructions including ROM, RAM, flash memory, and the like. In addition, examples of other media include an application store for distributing applications, a site for distributing or distributing various software, and a recording medium or storage medium managed by a server.

As described above, although the embodiments have been described by a limited embodiment and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques are performed in a different order than the described method, and/or the components of the described system, structure, device, circuit, etc. are combined or combined in a different form from the described method, or other components Alternatively, even if substituted or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (13)

A method for diagnosing a transformer failure performed in a computer device,
Detecting, by at least one processor, a transformer sound section from sound data collected by the power transformer;
Removing harmonics from the transformer sound section by the at least one processor;
Converting, by the at least one processor, the harmonic removed transformer sound to a sound intensity level; And
Diagnosing a failure of the power transformer using the sound intensity by the at least one processor.
Including,
The detecting step,
Removing white noise from the sound data using a smooth filter that is a low pass filter; And
Classifying the transformer sound section from the sound data from which the white noise is removed by applying a support vector data description (SVDD)
Including,
The classification step,
The SVDD model in which the actual sound of the transformer without noise is learned is applied by a sliding window processing method to classify the transformer sound section from the sound data from which the white noise has been removed,
In order to apply the SVDD in sound streaming, the transformer sound section is detected by applying the sliding window processing method so that the length has a variable parameter,
The diagnosis step,
To determine whether the power transformer has failed by comparing the average, maximum, and minimum statistical values of the sound intensity with the predetermined average, maximum, and minimum statistical values of the normal transformer sound intensity.
Transformer fault diagnosis method characterized in that. According to claim 1,
The detecting step,
Collecting the sound data wirelessly through the power transformer or a microphone sensor attached to the periphery of the power transformer
Transformer fault diagnosis method comprising a. According to claim 1,
The detecting step,
Verifying the transformer sound section classified through the SVDD using a convolutional neural network (CNN)
Transformer fault diagnosis method further comprising a. delete According to claim 1,
The removing step,
Removing harmonics in the sound section of the transformer using a low pass filter
Transformer fault diagnosis method characterized in that. delete A computer program stored in a computer-readable recording medium for executing a transformer failure diagnosis method,
The transformer fault diagnosis method,
Detecting a sound section of the transformer from sound data collected by the power transformer;
Removing harmonics from the transformer sound section;
Converting the transformer sound from which the harmonics have been removed into a sound intensity level; And
Diagnosing the failure of the power transformer using the sound intensity
Including,
The detecting step,
Removing white noise from the sound data using a smooth filter that is a low pass filter; And
Classifying the transformer sound section from the sound data from which the white noise is removed by applying a support vector data description (SVDD)
Including,
The classification step,
The SVDD model in which the actual sound of the transformer without noise is learned is applied by a sliding window processing method to classify the transformer sound section from the sound data from which the white noise has been removed,
In order to apply the SVDD in sound streaming, the transformer sound section is detected by applying the sliding window processing method so that the length has a variable parameter,
The diagnosis step,
To determine whether the power transformer has failed by comparing the average, maximum, and minimum statistical values of the sound intensity with the predetermined average, maximum, and minimum statistical values of the normal transformer sound intensity.
Characterized in that, computer program stored in a computer-readable recording medium. In the computer implemented transformer fault diagnosis system,
At least one processor configured to execute computer readable instructions contained in memory
Including,
The at least one processor,
A sound detector for detecting a transformer sound section from sound data collected by a power transformer;
A harmonic removing unit that removes harmonics from the transformer sound section;
An intensity converter for converting the transformer sound from which the harmonics have been removed into sound intensity levels; And
Transformer diagnosis unit for diagnosing the failure of the power transformer using the sound intensity
Including,
The sound detection unit,
White noise is removed from the sound data using a smooth filter, which is a low pass filter,
Applying SVDD (Support Vector Data Description) to classify the transformer sound section from the sound data from which the white noise has been removed,
The SVDD model in which the actual sound of the transformer without noise is learned is applied by a sliding window processing method to classify the transformer sound section from the sound data from which the white noise has been removed,
In order to apply the SVDD in sound streaming, the transformer sound section is detected by applying the sliding window processing method so that the length has a variable parameter,
The transformer diagnostic unit,
To determine whether the power transformer has failed by comparing the average, maximum, and minimum statistical values of the sound intensity with the predetermined average, maximum, and minimum statistical values of the normal transformer sound intensity.
Transformer fault diagnosis system, characterized by. The method of claim 8,
The sound detection unit,
Collecting the sound data wirelessly through the power transformer or a microphone sensor attached to the periphery of the power transformer
Transformer fault diagnosis system, characterized by. The method of claim 8,
The sound detection unit,
Verifying the transformer sound section classified through the SVDD using a convolutional neural network (CNN)
Transformer fault diagnosis system, characterized by. delete The method of claim 8,
The harmonic removal unit,
Removing harmonics in the sound section of the transformer using a low pass filter
Transformer fault diagnosis system, characterized by. delete

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