CN116592994A - Transformer operation monitoring method and device and electronic equipment - Google Patents
Transformer operation monitoring method and device and electronic equipment Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 74
- 238000012544 monitoring process Methods 0.000 title claims abstract description 24
- 230000005236 sound signal Effects 0.000 claims abstract description 282
- 238000012545 processing Methods 0.000 claims abstract description 45
- 230000008569 process Effects 0.000 claims abstract description 37
- 230000002159 abnormal effect Effects 0.000 claims abstract description 22
- 238000012806 monitoring device Methods 0.000 claims abstract description 15
- 230000004044 response Effects 0.000 claims abstract description 11
- 238000012790 confirmation Methods 0.000 claims abstract description 6
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- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 7
- 230000010363 phase shift Effects 0.000 claims description 7
- 238000001228 spectrum Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000007689 inspection Methods 0.000 abstract description 11
- 238000004364 calculation method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 5
- 238000013135 deep learning Methods 0.000 description 5
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Abstract
The application provides a transformer operation monitoring method, a device and electronic equipment, wherein the method comprises the following steps: acquiring sound emitted in the operation process of the transformer, wherein the sound comprises the following components: multipath audio signals collected by the sound pickup equipment; processing each path of audio signals in the multipath audio signals to obtain sound intensity characteristic values of each path of audio signals and characteristic values of sound signals to be detected, and primarily identifying whether the sound signals are abnormal sounds or not; and determining that the transformer fails in response to the confirmation that the sound signal to be detected is abnormal sound, and determining the failure type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected. By the transformer operation monitoring method, the transformer operation monitoring device and the electronic equipment, the purpose of judging whether the transformer has faults or not without manual inspection is achieved.
Description
Technical Field
The application relates to the technical field of transformer operation monitoring, in particular to a transformer operation monitoring method, a device and electronic equipment.
Background
At present, high-voltage transformers and ultrahigh-voltage transformer equipment are generally uniformly arranged in a large-scale transformer field. Various surface-mounted sensors cannot be used due to the high voltage properties of high voltage transformers and ultra-high voltage transformer devices. Thus, the operating state of the transformer can only be far-field monitored using various far-field monitoring schemes.
The traditional transformer far-field monitoring scheme judges the faults of the transformer by sending out the inspection listening sound of the workers with abundant experience, and the number of experienced workers is not increased along with the rapid increase of the number of the transformers in recent years, so that the real-time detection of whether the transformers have obvious vibration, noise change and discharge sound is ensured, and the transformer is a short board for transformer overhaul and maintenance.
Disclosure of Invention
In order to solve the above problems, an embodiment of the present application is to provide a method, an apparatus, and an electronic device for monitoring operation of a transformer.
In a first aspect, an embodiment of the present application provides a method for monitoring operation of a transformer, including:
acquiring sound emitted in the operation process of the transformer, wherein the sound comprises the following components: multipath audio signals collected by the sound pickup equipment;
processing each path of audio signals in the multipath audio signals to obtain sound intensity characteristic values of each path of audio signals and characteristic values of sound signals to be detected; the sound intensity characteristic value is used for representing the time domain intensity of each path of audio signals;
and determining that the transformer fails when the sound signal to be detected is abnormal sound, and determining the failure type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected.
In a second aspect, an embodiment of the present application further provides a transformer operation monitoring device, including:
the acquisition module is used for acquiring sound emitted in the operation process of the transformer, and the sound comprises the following components: multipath audio signals collected by the sound pickup equipment;
the processing module is used for processing each path of audio signals in the multipath audio signals to obtain sound intensity characteristic values of each path of audio signals and characteristic values of sound signals to be detected; the sound intensity characteristic value is used for representing the time domain intensity of each path of audio signals;
and the determining module is used for determining that the transformer fails in response to the confirmation that the sound signal to be detected is abnormal sound, and determining the failure type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected.
In a third aspect, embodiments of the present application also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect described above.
In a fourth aspect, an embodiment of the present application further provides an electronic device, where the electronic device includes a memory, a processor, and one or more programs, where the one or more programs are stored in the memory and configured to execute the steps of the method described in the first aspect by the processor.
In the solutions provided in the first to fourth aspects of the embodiments of the present application, by processing each path of audio signals in the multiple paths of audio signals collected by the pickup device during the operation of the transformer, a sound intensity characteristic value of each path of audio signal and a characteristic value of a sound signal to be detected are obtained, and whether the sound signal is abnormal sound is primarily identified; in response to abnormal sound of the sound signal to be detected, determining that the transformer fails, determining the type of the failure of the transformer by utilizing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, and compared with the mode of judging the failure of the transformer by sending out a worker with abundant experience to carry out inspection listening in the related art, judging whether the transformer fails or not without manual inspection by processing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, which are obtained in the multi-path audio signal operation process of the transformer acquired by the pickup device; in addition, faults generated in the operation of the transformer can be detected uninterruptedly, and the fault detection efficiency of the transformer is greatly improved.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a transformer operation monitoring method according to embodiment 1 of the present application;
fig. 2 is a schematic structural diagram of a transformer operation monitoring device according to embodiment 2 of the present application;
fig. 3 shows a schematic structural diagram of an electronic device according to embodiment 3 of the present application.
Detailed Description
In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
At present, high-voltage transformers and ultrahigh-voltage transformer equipment are generally uniformly arranged in a large-scale transformer field. Various surface-mounted sensors cannot be used due to the high voltage properties of high voltage transformers and ultra-high voltage transformer devices. Thus, the operating state of the transformer can only be far-field monitored using various far-field monitoring schemes.
The traditional transformer far-field monitoring scheme judges the faults of the transformer by sending out the inspection listening sound of the workers with abundant experience, and the number of experienced workers is not increased along with the rapid increase of the number of the transformers in recent years, so that the real-time detection of whether the transformers have obvious vibration, noise change and discharge sound is ensured, and the transformer is a short board for transformer overhaul and maintenance.
Based on this, the following embodiments of the present application provide a method, an apparatus, and an electronic device for monitoring operation of a transformer, where, by processing each path of audio signals in multiple paths of audio signals collected by a pickup device during operation of the transformer, a sound intensity characteristic value of each path of audio signal and a characteristic value of a sound signal to be detected are obtained, and whether the sound signal is abnormal sound is primarily identified; in response to abnormal sound of the sound signal to be detected, determining that the transformer fails, determining the type of the transformer failure by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected, and judging whether the transformer fails or not by processing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected, which are obtained after processing each path of audio signal in the multi-path audio signal of the transformer operation process acquired by the pickup device, so that the purpose of judging whether the transformer fails or not without manual inspection is achieved.
In order that the above-recited objects, features and advantages of the present application will become more apparent, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
Example 1
The main execution body of the transformer operation monitoring method proposed in the present embodiment is a monitoring device disposed near the transformer.
In one embodiment, the monitoring device may be fixed to the bottom of the transformer and not in contact with the transformer.
The monitoring device includes: a processor, a wireless communication module, and a plurality of sound pickup devices; the processor is connected with the wireless communication module and the plurality of pickup devices respectively.
The sound that a plurality of pickup equipment were towards the transformer, to the transformer in the operation in-process sent is gathered to gather multichannel audio signal. Each of the plurality of sound pickup devices can collect one path of audio signals in sound.
Components of the transformer that may emit sound during operation include, but are not limited to: the cooling fan comprises an oil tank, windings, an iron core, a cooling fan and a bearing of the cooling fan.
The monitoring device interacts with the cloud server through the wireless communication module.
Pickup devices, including but not limited to: microphone, recorder, recording pen and microphone.
Referring to a flowchart of a transformer operation monitoring method shown in fig. 1, the present embodiment proposes a transformer operation monitoring method, which includes the following specific steps:
step 100, obtaining sounds generated in the operation process of the transformer, wherein the sounds comprise: and the pickup equipment collects multiple paths of audio signals.
In the step 100, after the monitoring device acquires the multiple paths of audio signals collected by the pickup device, the monitoring device performs preprocessing such as analog-to-digital conversion and noise reduction on the multiple paths of audio signals, and then sends the preprocessed multiple paths of audio signals to the processor for processing.
The process of the monitoring device for performing the preprocessing such as the analog-to-digital conversion and the noise reduction on the multi-path audio signal can adopt the existing technical means for performing the preprocessing such as the analog-to-digital conversion and the noise reduction on the multi-path audio signal, and the description is omitted here.
102, processing each path of audio signals in the multipath audio signals to obtain sound intensity characteristic values of each path of audio signals and characteristic values of sound signals to be detected; the sound intensity characteristic value is used for representing the time domain intensity of each path of audio signals.
In step 102, to obtain the intensity characteristic values of each audio signal, the processor may execute the following steps:
and respectively carrying out multiple variance operation on each path of audio signals to obtain multiple variance operation results of each path of audio signals, and carrying out weighting operation processing on the multiple variance operation results of each path of audio signals to obtain sound intensity characteristic values of each path of audio signals.
In the above steps, in one embodiment, four variance operations may be performed on each audio signal, where the first forward variance operation is 0 and the interval is 1, the second forward variance operation is 1, the third reverse variance operation is 1, and the fourth forward variance operation is 2.
The forward variance operation refers to that the processor carries out variance processing on each path of audio signals according to the sequence of inputting each path of audio signals into the processor.
The reverse variance operation means that the processor carries out variance processing on each path of audio signals according to the reverse order of the input of each path of audio signals to the processor.
Spacing, which refers to the number of binary numbers between each path of audio signals; the interval is 0, which means that the number of binary numbers between the audio signals is 0; the interval is n, which means that the number of binary numbers between the audio signals is n, where n is a natural number.
In the process of performing weighted operation processing on a plurality of variance operation results of each path of audio signal, in order to ensure the accuracy of the fault type of the transformer determined by the transformer operation monitoring method provided by the embodiment, weights need to be allocated to four variance operation results of each path of audio signal, and the mode of allocating the weights is as follows:
the weight of the first forward variance operation result of each path of audio signal is far greater than that of the third forward variance operation result of each path of audio signal, and the weight of the second forward variance operation result of each path of audio signal is greater than that of the third backward variance operation result of each path of audio signal and that of the fourth forward variance operation result of each path of audio signal.
Illustratively, the weight of the first forward variance operation result of each audio signal may be set to 0.8; the weight of the second forward variance operation result of each path of audio signal can be set to 0.1; the weight of the third reverse variance operation result of each audio signal and the weight of the fourth forward variance operation result of each audio signal may be set to 0.05, respectively.
The specific process of performing the average weighting operation on the variance operation results of each path of audio signal to obtain the sound intensity characteristic value of each path of audio signal can adopt the existing technical means of the average weighting operation, and will not be described herein.
In order to obtain the feature quantity of the sound signal to be detected, the processor may further execute the following steps (1) to (5): (1) Processing the multipath audio signals by using a non-average algorithm to obtain a sound signal to be detected;
(2) Filtering a sound signal to be detected to obtain a filtered signal of the sound signal;
(3) Performing 90-degree phase shift processing on the filtered signal to obtain a phase shift signal of the sound signal;
(4) Mapping and converting the phase-shift signal to obtain a mapping result of the sound signal, and performing Fourier conversion on the mapping result to obtain a frequency spectrum signal of the sound signal;
(5) And processing the frequency spectrum signal of the sound signal to obtain the characteristic quantity of the sound signal.
In the above step (1), in order to process the multi-channel audio signal using the non-average algorithm to obtain the sound signal to be detected, the following steps (11) to (12) may be performed:
(11) Mapping conversion is carried out on the multipath audio signals to obtain multipath mapping signals;
(12) And carrying out weighted operation on the multipath mapping signals to obtain the sound signal to be detected.
In the step (11), in order to enhance the weak signal in the multi-path audio signal, the subsequent extraction of the weak signal in the multi-path audio signal is facilitated, and a logarithmic curve 0.23×ln (1+71) is selected x ) As a mapping function.
The specific process of mapping and converting the multi-path audio signal to obtain the multi-path mapping signal can adopt the existing technical means for mapping and converting the multi-path audio signal, and will not be repeated here.
In the step (12), the multipath mapping signals are weighted to obtain the sound signal to be detected, and the technical means of the existing weighting operation process can be adopted, which is not described herein.
In one embodiment, in order to ensure the accuracy of the fault type of the transformer determined by the transformer operation monitoring method provided in this embodiment, in the process of performing the weighting operation on the multiple mapping signals, a weight of 0.8 is allocated to the mapping signal with the largest frequency amplitude in the multiple mapping signals, and the remaining weights of 0.2 are evenly allocated to the mapping signals other than the mapping signal with the largest frequency amplitude in the multiple mapping signals.
In the step (2), in one embodiment, in order to filter the clutter in the sound signal, the filtering range of filtering the sound signal is 50 hz to 20000 hz.
The processor filters the sound signal using a filtering algorithm to obtain a filtered signal of the sound signal.
The specific process of filtering the sound signal by the processor using the filtering algorithm to obtain the filtered signal of the sound signal may adopt the existing technical means of filtering the sound signal to obtain the filtered signal of the sound signal, which will not be described herein.
In the step (3), in order to obtain more effective extraction of the frequency components of the signal, the filtered signal is subjected to a 90-degree phase shift process to obtain a phase shifted signal of the sound signal.
In the step (4), in the process of mapping and converting the phase-shifted signal to obtain the mapping result of the sound signal, a symbol function is selected as the mapping function in order to perform nonlinear screening on the phase-shifted signal.
The specific process of mapping the phase-shifted signal to obtain the mapping result of the sound signal may use the existing mapping conversion technique, which is not described herein.
In the step (5), the feature amount of the sound signal includes: the specific frequency of the sound signal to be detected, the reference frequency band peak value of the sound signal to be detected, the frequency peak ratio of the sound signal to be detected and the specific frequency quantity of the sound signal to be detected.
In order to obtain the feature quantity of the sound signal to be detected, the following steps (51) to (54) may be performed:
(51) Extracting a reference frequency of transformer operation from the frequency spectrum signal of the sound signal to be detected, performing frequency multiplication operation on the reference frequency, and determining at least two specific frequencies;
(52) Determining the amplitude of the reference frequency based on the acquired reference frequency, and determining the amplitude of the reference frequency as a reference frequency band peak value of the sound signal to be detected;
(53) Determining the amplitude of each specific frequency in the at least two specific frequencies, dividing the amplitude of each specific frequency by the amplitude of the reference frequency, calculating the frequency peak ratio of each specific frequency, and determining the calculated frequency peak ratio of each specific frequency as the frequency peak ratio of the sound signal to be detected;
(54) And determining the specific frequency with the frequency peak ratio larger than a Yu Pinfeng ratio threshold value in the at least two specific frequencies as the specific frequency of the sound signal to be detected, and counting to obtain the number of the specific frequencies of the sound signal to be detected.
In step (51), in one embodiment, the reference frequency of the transformer is 2 times the national grid frequency and is 100 hz, as will be appreciated by those skilled in the transformer art.
Based on different fault types, at least two specific frequencies may be: 300 Hz, 500 Hz and 700 Hz. It is also possible that: 200 Hz, 300 Hz, 400 Hz and 500 Hz.
Of course, the at least two specific frequencies may also be frequency multiplied by other reference frequencies, which are not described in detail herein, based on other fault types.
In the step (52), based on the obtained reference frequency, the specific process of determining the amplitude of the reference frequency may adopt an existing technical means of extracting the amplitude from the signal frequency, which is not described herein.
In the step (53), the specific process of determining the amplitude of each specific frequency of the at least two specific frequencies may use the existing technical means of extracting the amplitude from the signal frequency, which is not described herein.
In step (54) above, the peak-to-average ratio threshold is pre-cached in the processor.
Optionally, after the sound intensity characteristic value of each path of audio signal and the characteristic value of the sound signal to be detected are obtained through the step 102, a common method for detecting the singularity in the signal processing field may be further adopted to obtain the singularity characteristic value of each path of audio signal, and in the transformer operation monitoring method provided in this embodiment, the singularity characteristic value of each path of audio signal may be further obtained through the following steps:
and respectively performing singularity detection on each path of audio signals in the multipath audio signals included in the sound to obtain the singularity characteristic value of each path of audio signals.
In the above steps, the processor performs the singular detection on each path of audio signal, and the specific process of obtaining the singular characteristic value of each path of audio signal may adopt the existing technical means of obtaining the singular characteristic value of each path of audio signal, which is not described herein again.
Of course, the process of obtaining the singular characteristic value of each path of audio signal may also be performed before the step 102 is performed to obtain the sound intensity characteristic value of each path of audio signal and the characteristic value of the sound signal to be detected, and in this embodiment, the process of obtaining the singular characteristic value of each path of audio signal and the sequence of obtaining the sound intensity characteristic value of each path of audio signal and the characteristic value of the sound signal to be detected in the step 102 are not limited.
After the intensity characteristic values of the audio signals and the characteristic values of the sound signals to be detected are obtained through the above step 102, the following step 104 may be continuously performed to determine the fault type of the transformer.
And 104, determining that the transformer fails in response to the confirmation that the sound signal is abnormal sound, and determining the failure type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected.
Specifically, in order to determine that the transformer fails, the above step 104 may perform the following steps (1) to (2):
(1) Comparing the counted number of the specific frequencies with the number threshold value to obtain a comparison result;
(2) And when the comparison result indicates that the number of the specific frequencies is larger than a number threshold, obtaining a result that the sound signal is abnormal sound, and determining that the transformer fails.
In step (1) above, the number threshold is pre-cached in the processor.
After determining that the transformer fails, the above step 104 may further perform the following steps (3) to (4):
(3) Inputting the sound intensity characteristic value and the singularity characteristic value of each path of audio signal and the characteristic quantity of the sound signal into a pre-trained transformer fault classification model, and processing the sound intensity characteristic value and the singularity characteristic value of each path of audio signal and the characteristic quantity of the sound signal through the transformer fault classification model to determine the fault type of the transformer;
(4) When the comparison result indicates that the number of the specific frequencies is less than or equal to a number threshold, a result that the sound signal is a normal sound is obtained and the flow is ended.
In the step (3), a pre-trained transformer fault classification model is run in the processor.
The transformer fault classification model is obtained by inputting known transformer fault types and matched audio signals into a deep learning calculation model and training the deep learning calculation model.
The specific process of training the deep learning calculation model to obtain the transformer fault classification model can adopt the existing technical means for training the deep learning calculation model, and the description is omitted here.
The sound intensity characteristic value and the singular characteristic value of each path of audio signals and the characteristic quantity of the sound signals are processed through the transformer fault classification model, and the specific process of determining the fault type of the transformer can adopt the existing deep learning calculation model to classify the fault type to obtain the technical means of the fault type of the transformer, and the description is omitted here.
The fault types of transformers include, but are not limited to: iron core vibration failure, winding vibration failure, tank wall vibration failure, and bearing failure.
After determining the fault type of the transformer, the processor feeds back the determined fault type of the transformer and the acquired multipath audio signals to the cloud server by using the wireless communication module.
In summary, the present embodiment provides a method for monitoring operation of a transformer, which processes each path of audio signals in multiple paths of audio signals collected by a pickup device during operation of the transformer to obtain a sound intensity characteristic value of each path of audio signals and a characteristic quantity of a sound signal to be detected, and primarily identifies whether the sound signal is abnormal; in response to confirming that the sound signal to be detected is abnormal sound, determining that the transformer has faults, determining the fault type of the transformer by utilizing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, and compared with the mode of judging the faults of the transformer by dispatching workers with abundant experience in the related art, judging whether the transformer has faults or not without manual inspection by processing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, which are acquired by the pickup device, in the multi-path audio signal of the transformer operation process; in addition, faults generated in the operation of the transformer can be detected uninterruptedly, and the fault detection efficiency of the transformer is greatly improved.
Example 2
The present embodiment provides a transformer operation monitoring device for executing the transformer operation monitoring method set forth in embodiment 1.
Referring to a schematic structural diagram of a transformer operation monitoring device shown in fig. 2, this embodiment provides a transformer operation monitoring device, including:
the obtaining module 200 is configured to obtain a sound emitted during the operation of the transformer, where the sound includes: multipath audio signals collected by the sound pickup equipment;
the processing module 202 is configured to process each path of audio signals in the multiple paths of audio signals to obtain a sound intensity characteristic value of each path of audio signals and a characteristic value of a sound signal to be detected; the sound intensity characteristic value is used for representing the time domain intensity of each path of audio signals;
and the determining module 204 is configured to determine that the transformer fails in response to the sound signal to be detected being abnormal, and determine a failure type of the transformer by using the sound intensity characteristic value of each path of audio signal and the characteristic value of the sound signal to be detected.
Specifically, the processing module is configured to process each path of audio signals in the multiple paths of audio signals to obtain a sound intensity characteristic value of each path of audio signals, and includes:
and respectively carrying out multiple variance operation on each path of audio signals to obtain multiple variance operation results of each path of audio signals, and carrying out weighting operation processing on the multiple variance operation results of each path of audio signals to obtain sound intensity characteristic values of each path of audio signals.
In summary, the present embodiment provides a transformer operation monitoring device, which processes each path of audio signals in multiple paths of audio signals collected by a pickup device in a transformer operation process to obtain a sound intensity characteristic value of each path of audio signals and a characteristic quantity of a sound signal to be detected, and primarily identifies whether the sound signal is abnormal sound; in response to abnormal sound of the sound signal to be detected, determining that the transformer fails, determining the type of the failure of the transformer by utilizing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, and compared with the mode of judging the failure of the transformer by sending out a worker with abundant experience to carry out inspection listening in the related art, judging whether the transformer fails or not without manual inspection by processing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, which are obtained in the multi-path audio signal operation process of the transformer acquired by the pickup device; in addition, faults generated in the operation of the transformer can be detected uninterruptedly, and the fault detection efficiency of the transformer is greatly improved.
Example 3
The present embodiment proposes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the transformer operation monitoring method described in embodiment 1 above. The specific implementation can be referred to method embodiment 1, and will not be described herein.
In addition, referring to the schematic structural diagram of an electronic device shown in fig. 3, the present embodiment also proposes an electronic device, which includes a bus 51, a processor 52, a transceiver 53, a bus interface 54, a memory 55, and a user interface 56. The electronic device includes a memory 55.
In this embodiment, the electronic device further includes: one or more programs stored on memory 55 and executable on processor 52, configured to be executed by the processor for performing steps (1) through (3) below:
(1) Acquiring sound emitted in the operation process of the transformer, wherein the sound comprises the following components: multipath audio signals collected by the sound pickup equipment;
(2) Processing each path of audio signals in the multipath audio signals to obtain sound intensity characteristic values of each path of audio signals and characteristic values of sound signals to be detected; the sound intensity characteristic value is used for representing the time domain intensity of each path of audio signals;
(3) And responding to the confirmation that the sound signal to be detected is abnormal sound, determining that the transformer fails, and determining the failure type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected.
A transceiver 53 for receiving and transmitting data under the control of the processor 52.
Where bus architecture (represented by bus 51), bus 51 may comprise any number of interconnected buses and bridges, with bus 51 linking together various circuits, including one or more processors, represented by processor 52, and memory, represented by memory 55. The bus 51 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art, and therefore, will not be described further in connection with this embodiment. Bus interface 54 provides an interface between bus 51 and transceiver 53. The transceiver 53 may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 53 receives external data from other devices. The transceiver 53 is used to transmit the data processed by the processor 52 to other devices. Depending on the nature of the computing system, a user interface 56 may also be provided, such as a keypad, display, speaker, microphone, joystick.
The processor 52 is responsible for managing the bus 51 and general processing, as described above, running the general-purpose operating system 551. And memory 55 may be used to store data used by processor 52 in performing operations.
Alternatively, processor 52 may be, but is not limited to: a central processing unit, a single chip microcomputer, a microprocessor or a programmable logic device.
It will be appreciated that the memory 55 in embodiments of the application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DRRAM). The memory 55 of the system and method described in this embodiment is intended to comprise, without being limited to, these and any other suitable types of memory.
In some implementations, the memory 55 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: operating system 551 and application programs 552.
The operating system 551 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs 552 include various application programs such as a Media Player (Media Player), a Browser (Browser), and the like for implementing various application services. A program for implementing the method of the embodiment of the present application may be included in the application program 552.
In summary, the present embodiment provides a computer readable storage medium and an electronic device, where, by processing each path of audio signals in multiple paths of audio signals collected by a pickup device in a transformer operation process, a sound intensity characteristic value of each path of audio signals and a characteristic value of a sound signal to be detected are obtained, and whether the sound signal is abnormal sound is primarily identified; in response to abnormal sound of the sound signal to be detected, determining that the transformer fails, determining the type of the failure of the transformer by utilizing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, and compared with the mode of judging the failure of the transformer by sending out a worker with abundant experience to carry out inspection listening in the related art, judging whether the transformer fails or not without manual inspection by processing the sound intensity characteristic value of each path of the audio signal and the characteristic quantity of the sound signal to be detected, which are obtained in the multi-path audio signal operation process of the transformer acquired by the pickup device; in addition, faults generated in the operation of the transformer can be detected uninterruptedly, and the fault detection efficiency of the transformer is greatly improved.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for monitoring operation of a transformer, comprising:
acquiring sound emitted in the operation process of the transformer, wherein the sound comprises the following components: multipath audio signals collected by the sound pickup equipment;
processing each path of audio signals in the multipath audio signals to obtain sound intensity characteristic values of each path of audio signals and characteristic values of sound signals to be detected; the sound intensity characteristic value is used for representing the time domain intensity of each path of audio signals;
and responding to the confirmation that the sound signal to be detected is abnormal sound, determining that the transformer fails, and determining the failure type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected.
2. The method of claim 1, wherein processing each of the plurality of audio signals to obtain a sound intensity characteristic value of each of the plurality of audio signals comprises:
and carrying out multiple variance operation on each path of audio signals to obtain multiple variance operation results of each path of audio signals, and carrying out weighting operation processing on the multiple variance operation results of each path of audio signals to obtain sound intensity characteristic values of each path of audio signals.
3. The method according to claim 1, wherein processing each of the plurality of audio signals to obtain a feature value of a sound signal to be detected comprises:
processing the multipath audio signals by using a non-average algorithm to obtain a sound signal to be detected;
filtering the sound signal to be detected to obtain a filtered signal of the sound signal to be detected;
performing 90-degree phase shift processing on the filtering signal to obtain a phase shift signal of the sound signal to be detected;
mapping and converting the phase shift signal to obtain a mapping result of the sound signal to be detected, and performing Fourier conversion on the mapping result to obtain a frequency spectrum signal of the sound signal to be detected;
and processing the frequency spectrum signal of the sound signal to be detected to obtain the characteristic quantity of the sound signal to be detected.
4. A method according to claim 3, wherein the characteristic quantity of the sound signal to be detected comprises: the specific frequency of the sound signal to be detected, the reference frequency band peak value of the sound signal to be detected, the frequency peak ratio of the sound signal to be detected and the specific frequency quantity of the sound signal to be detected;
processing the frequency spectrum signal of the sound signal to be detected to obtain the characteristic quantity of the sound signal to be detected, wherein the processing comprises the following steps:
extracting a reference frequency of transformer operation from the frequency spectrum signal of the sound signal to be detected, performing frequency multiplication operation on the reference frequency, and determining at least two specific frequencies;
determining the amplitude of the reference frequency based on the acquired reference frequency, and determining the amplitude of the reference frequency as a reference frequency band peak value of the sound signal to be detected;
determining the amplitude of each specific frequency in the at least two specific frequencies, dividing the amplitude of each specific frequency by the amplitude of the reference frequency, calculating the frequency peak ratio of each specific frequency, and determining the calculated frequency peak ratio of each specific frequency as the frequency peak ratio of the sound signal to be detected;
and determining the specific frequency with the frequency peak ratio larger than a Yu Pinfeng ratio threshold value in the at least two specific frequencies as the specific frequency of the sound signal to be detected, and counting to obtain the number of the specific frequencies of the sound signal to be detected.
5. The method of claim 4, wherein determining that the transformer is malfunctioning in response to determining that the acoustic signal is abnormal comprises:
comparing the counted number of the specific frequencies with the number threshold value to obtain a comparison result;
and when the comparison result indicates that the number of the specific frequencies is larger than a number threshold, obtaining a result that the sound signal is abnormal sound, and determining that the transformer fails.
6. The method of claim 1, wherein after capturing the sound emitted during operation of the transformer, the method further comprises:
performing singularity detection on each path of audio signals in the multipath audio signals included in the sound to obtain a singularity characteristic value of each path of audio signals;
determining the fault type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected, wherein the method comprises the following steps: and inputting the sound intensity characteristic value and the singular characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected into a pre-trained transformer fault classification model, and processing the sound intensity characteristic value and the singular characteristic value of each path of audio signal and the characteristic quantity of the sound signal through the transformer fault classification model to determine the fault type of the transformer.
7. A transformer operation monitoring device, comprising:
the acquisition module is used for acquiring sound emitted in the operation process of the transformer, and the sound comprises the following components: multipath audio signals collected by the sound pickup equipment;
the processing module is used for processing each path of audio signals in the multipath audio signals to obtain sound intensity characteristic values of each path of audio signals and characteristic values of sound signals to be detected; the sound intensity characteristic value is used for representing the time domain intensity of each path of audio signals;
and the determining module is used for determining that the transformer fails in response to the confirmation that the sound signal to be detected is abnormal sound, and determining the failure type of the transformer by utilizing the sound intensity characteristic value of each path of audio signal and the characteristic quantity of the sound signal to be detected.
8. The apparatus of claim 7, wherein the processing module is configured to process each of the multiple audio signals to obtain a sound intensity characteristic value of each of the multiple audio signals, and comprises:
and respectively carrying out multiple variance operation on each path of audio signals to obtain multiple variance operation results of each path of audio signals, and carrying out weighting operation processing on the multiple variance operation results of each path of audio signals to obtain sound intensity characteristic values of each path of audio signals.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor performs the steps of the method of any of the preceding claims 1-6.
10. An electronic device comprising a memory, a processor, and one or more programs, wherein the one or more programs are stored in the memory and configured to perform the steps of the method of any of claims 1-6 by the processor.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117232644A (en) * | 2023-11-13 | 2023-12-15 | 国网吉林省电力有限公司辽源供电公司 | Transformer sound monitoring fault diagnosis method and system based on acoustic principle |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117232644A (en) * | 2023-11-13 | 2023-12-15 | 国网吉林省电力有限公司辽源供电公司 | Transformer sound monitoring fault diagnosis method and system based on acoustic principle |
| CN117232644B (en) * | 2023-11-13 | 2024-01-09 | 国网吉林省电力有限公司辽源供电公司 | Transformer sound monitoring fault diagnosis method and system based on acoustic principle |
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