An identification method and system for a dry-type transformer with secondary silicon steel sheets

Abstract

The present invention discloses a method and system for identifying a secondary silicon steel sheet dry-type transformer. The method comprises the following steps: performing no-load tests on a dry-type transformer to be identified and a brand-new dry-type transformer of the same model, and collecting two noise audio segments emitted by the two dry-type transformers at this time; performing short-time Fourier analysis on the noise audio to obtain the sound wave amplitude corresponding to each frequency, taking the 50 Hz frequency-multiplied sound wave amplitude therein to calculate the fundamental frequency energy proportion, spectrum complexity, and high-to-low frequency energy ratio of the dry-type transformer; calculating the deviation rate of the fundamental frequency energy proportion, spectrum complexity, and high-to-low frequency energy ratio between the brand-new dry-type transformer and the transformer to be identified, and judging whether the transformer to be identified is a secondary sheet transformer by comparing the deviation rate with a set threshold. The system and the method are based on the same concept. The present invention efficiently and accurately judges whether a transformer is a secondary silicon steel sheet transformer without disassembling the transformer, thereby improving the inspection efficiency of the transformer and reducing the labor and material costs required for random inspections.

Description

Technical Field

[0001] The present invention relates to transformer silicon steel sheet detection technology, and in particular to an identification method and system for a secondary silicon steel sheet dry-type transformer. Background Art

[0002] The transformer is one of the key equipment in the power system. Its operating status is directly related to the safe and stable operation of the entire power system. The iron core composed of silicon steel sheets is the main component of the transformer. In order to reduce costs, some manufacturers use secondary silicon steel sheets to reprocess the transformer. After long-term operation and rough reprocessing, the characteristics of the secondary silicon steel sheets are very different from those of new silicon steel sheets. There are cases of serious damage to the surface coating, increased lamination factor, and abnormal loss. Therefore, secondary silicon steel sheet transformers often have problems such as loud and harsh noise and local overheating during operation. These problems can easily lead to local breakdown, combustion and explosion inside the transformer, posing a huge safety hazard. The identification problem of secondary silicon steel sheet transformers needs to be solved urgently.

[0003] CN106199282A "A method for identifying a secondary silicon steel sheet distribution transformer" provides a "method for identifying a secondary silicon steel sheet distribution transformer, including detecting the no-load loss P0 of the distribution transformer; controlling the distribution transformer to overexcite and operate for a first preset time and detecting its no-load loss P0'; judging whether the core of the distribution transformer uses inferior secondary silicon steel sheets based on the no-load loss P0' and the no-load loss P0". Compared with the prior art, the method for identifying a secondary silicon steel sheet transformer provided by this invention judges whether the core of the distribution transformer uses inferior secondary silicon steel sheets based on the no-load loss before and after the distribution transformer is overexcited. It can identify whether the distribution transformer uses inferior secondary silicon steel sheets without disassembling or destroying the distribution transformer and its core. However, this invention requires a long preset time during the detection process, the overall detection time is too long, and the actual implementation cost is high. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for identifying a secondary silicon steel sheet dry-type transformer in view of the defects in the prior art.

[0005] The technical solutions adopted by the present invention to solve the technical problems include:

[0006] A method for identifying a secondary silicon steel sheet dry-type transformer comprises the following steps:

[0007] Perform no-load tests on the dry-type transformer to be identified and a brand new dry-type transformer of the same model, and collect two audio noises emitted by the two dry-type transformers.

[0008] The two noise audio segments were analyzed by short-time Fourier transform to obtain the sound wave amplitude corresponding to each frequency of the two noise audio segments. The sound wave amplitude corresponding to the 50Hz multiple of the two noise audio segments was taken to calculate the fundamental frequency energy proportion, spectrum complexity and high-to-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer respectively.

[0009] According to the fundamental frequency energy ratio, spectrum complexity and high-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer, the deviation rate of the fundamental frequency energy ratio of the two is calculated. p 1. Deviation rate of spectral complexity p 2 and the deviation rate of high-low frequency energy ratio p 3. The deviation rate p 1. p 2 and p 3. Compare with the preset threshold value and determine whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result.

[0010] Preferably, the specific step of collecting the two noise audios emitted by the two dry-type transformers at this time is to use a condenser microphone to collect the noise emitted by the dry-type transformer during the no-load test, and the condenser microphone is set at the center of the front of the dry-type transformer and is 30 cm horizontally away from the center point of the transformer core.

[0011] Preferably, the fundamental frequency energy proportion, spectrum complexity, and high-frequency and low-frequency energy ratio are calculated using the following formula:

[0012] The expression of fundamental frequency energy ratio R is:

[0013]

[0014] in, A i for( i * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude;

[0015] The expression of spectral complexity H is:

[0016]

[0017] in, R i for( i *The energy density of 50)Hz, where n is the number of 50Hz multiples of the sound wave amplitude;

[0018] High-low frequency energy ratio The expression is:

[0019]

[0020] in, Ai for( i *50)Hz corresponding to the amplitude of the sound wave, A j for( j * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude.

[0021] Preferably, the deviation rate p 1. p 2 and p 3. Compare with the preset threshold value and determine whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result. Specifically:

[0022] When the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio are all greater than the set threshold, it is determined that the transformer to be identified is a secondary silicon steel sheet dry-type transformer;

[0023] When two of the deviation rates of the fundamental frequency energy ratio (p1), the spectrum complexity (p2), and the high-to-low frequency energy ratio (p3) exceed a set threshold, it is determined that the transformer to be identified may be a secondary silicon steel sheet dry-type transformer.

[0024] When only one of the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-to-low frequency energy ratio exceeds the set threshold, it is determined that the transformer to be identified is not a secondary silicon steel sheet dry-type transformer.

[0025] A secondary silicon steel sheet dry-type transformer identification system, comprising:

[0026] The acquisition module is used to perform no-load tests on the dry-type transformer to be identified and a new dry-type transformer of the same model and collect two noise audios emitted by the two dry-type transformers at this time;

[0027] The characteristic parameter calculation module is used to perform short-time Fourier transform analysis on the two noise audio segments to obtain the sound wave amplitude corresponding to each frequency of the two noise audio segments. The sound wave amplitude corresponding to the 50Hz multiple of the two noise audio segments is taken to calculate the fundamental frequency energy proportion, spectrum complexity and high-to-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer respectively.

[0028] The judgment module is used to calculate the deviation rate p1 of the fundamental frequency energy ratio, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio of the above-mentioned dry-type transformer to be identified and the new dry-type transformer based on the fundamental frequency energy ratio, spectrum complexity, and high-frequency and low-frequency energy ratio of the two, compare the deviation rates p1, p2, and p3 with the preset thresholds, and confirm whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result.

[0029] Preferably, the acquisition module includes a condenser microphone, which is used to collect the noise emitted by the dry-type transformer during the no-load test. The condenser microphone is arranged at the center of the front of the dry-type transformer and is 30 cm horizontally away from the center point of the transformer core.

[0030] Preferably, the fundamental frequency energy proportion, spectrum complexity, and high-frequency and low-frequency energy ratio are calculated using the following formula:

[0031] The expression of fundamental frequency energy ratio R is:

[0032]

[0033] in, A i for( i * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude;

[0034] The expression of spectral complexity H is:

[0035]

[0036] in, R i for( i *The energy density of 50)Hz, where n is the number of 50Hz multiples of the sound wave amplitude;

[0037] High-low frequency energy ratio The expression is:

[0038]

[0039] in, A i for( i *50)Hz corresponding to the amplitude of the sound wave, A j for( j * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude.

[0040] Preferably, the deviation rate p 1. p 2 and p 3. Compare with the preset threshold value and determine whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result. Specifically:

[0041] When the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio are all greater than the set threshold, it is determined that the transformer to be identified is a secondary silicon steel sheet dry-type transformer;

[0042] When two of the deviation rates of the fundamental frequency energy ratio (p1), the spectrum complexity (p2), and the high-to-low frequency energy ratio (p3) exceed a set threshold, it is determined that the transformer to be identified may be a secondary silicon steel sheet dry-type transformer.

[0043] When only one of the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-to-low frequency energy ratio exceeds the set threshold, it is determined that the transformer to be identified is not a secondary silicon steel sheet dry-type transformer.

[0044] An electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for identifying a secondary silicon steel sheet dry-type transformer according to any one of claims 1 to 4 is implemented.

[0045] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the identification method of a secondary silicon steel sheet dry-type transformer according to any one of claims 1 to 4.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present application provides a method and system for identifying a secondary silicon steel sheet dry-type transformer. The method only requires performing a no-load test on a dry-type transformer to be identified and a brand-new dry-type transformer of the same model and collecting two noise audio segments emitted by the two dry-type transformers at this time. The two noise audio segments are then analyzed, extracted, and compared to determine whether the transformer is a secondary silicon steel sheet transformer without disassembling the transformer. This improves the inspection efficiency of the transformer and reduces the labor cost required for random inspections.

[0048] 2. This application provides a method and system for identifying secondary silicon steel sheet dry-type transformers. Noise audio can be collected using only a condenser microphone, reducing the material cost required for random inspections.

[0049] 3. The present application provides a method and system for identifying a secondary silicon steel sheet dry-type transformer. Magnetostriction of the transformer silicon steel sheet is the main cause of noise, so the state of the silicon steel sheet has a decisive influence on the noise. The identification method based on the comparison of three noise characteristic values, namely, fundamental frequency energy proportion, spectrum complexity, and high- and low-frequency energy ratio, improves the accuracy of judging whether the transformer is a secondary silicon steel sheet transformer without disassembling the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0051] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0053] Example 1

[0054] This embodiment takes the noise audio during a no-load test of a dry-type transformer as the basis, considers three noise characteristic values: fundamental frequency energy proportion, spectral complexity, and high-to-low frequency energy ratio, and combines the comparison of their deviation rate with a preset threshold value to propose a method for identifying a secondary silicon steel sheet dry-type transformer. This method can determine whether a transformer is a secondary silicon steel sheet transformer without disassembling the transformer, thereby improving the inspection efficiency of the transformer, reducing the labor and material costs required for random inspections, and improving the accuracy of identification.

[0055] like Figure 1 As shown, this embodiment is a method for identifying a secondary silicon steel sheet dry-type transformer, comprising the following steps:

[0056] A no-load test is performed on the dry-type transformer to be identified and a new dry-type transformer of the same model, and two noise audios emitted by the two dry-type transformers are collected at this time; common microphones include condenser microphones and dynamic microphones. Condenser microphones have high sensitivity and can record sound quality with high restoration, while dynamic microphones have low sensitivity and are not easy to pick up environmental noise, so very little noise is generated. On this basis, the specific steps of collecting the two noise audios emitted by the two dry-type transformers at this time described in this embodiment are to use a condenser microphone to collect the noise emitted by the dry-type transformer during the no-load test. The condenser microphone is set at the center of the front of the dry transformer and is 30 cm horizontally away from the center point of the transformer core.

[0057] Since Fourier transform is only applicable to stationary signals, noise audio signals are non-stationary signals, and their frequency characteristics change with time. In order to capture this time-varying feature, it is necessary to perform time-frequency analysis on the signal. Short-time Fourier transform, wavelet transform, Hilbert-Huang transform, etc. are commonly used to perform time-frequency analysis on the signal. In this embodiment, the noise audio is subjected to short-time Fourier transform analysis to obtain the sound wave amplitude corresponding to each frequency, and the 50Hz multiple frequency sound wave amplitude is taken, and the 50Hz multiple frequency sound wave amplitude within 1000Hz is defined as A 1~ A 20 , calculate the fundamental frequency energy proportion, spectrum complexity and high-low frequency energy ratio. The specific formula is:

[0058] The expression of fundamental frequency energy ratio R is:

[0059]

[0060] in, A i for( i * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude;

[0061] The expression of spectral complexity H is:

[0062]

[0063] in, R i for( i *The energy density of 50)Hz, where n is the number of 50Hz multiples of the sound wave amplitude;

[0064] High-low frequency energy ratio The expression is:

[0065]

[0066] in, A i for( i *50)Hz corresponding to the amplitude of the sound wave, A j for( j * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude.

[0067] According to the fundamental frequency energy ratio, spectrum complexity and high-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer, the deviation rate of the fundamental frequency energy ratio of the two is calculated. p 1. Deviation rate of spectral complexity p 2 and the deviation rate of high-low frequency energy ratio p 3:

[0068] ， ，

[0069] Among them, the fundamental frequency energy proportion of the new dry-type transformer noise is expressed as R 1. Spectral complexity is expressed as H 1. The high-frequency and low-frequency energy ratio is expressed as R hl1 The fundamental frequency energy proportion of the dry-type transformer noise to be identified is expressed as R 2. Spectral complexity is expressed as H 2. The high-frequency and low-frequency energy ratio is expressed as R hl2 .

[0070] The deviation ratep 1. p 2 and p 3. Compare with the preset threshold. Preferably, after a large number of simulation experiments, the threshold is set as follows: the deviation rate of the fundamental frequency energy ratio p The threshold corresponding to 1 is 28%, and the deviation rate of spectrum complexity is p The threshold corresponding to 2 is 6%, and the deviation rate of the high-low frequency energy ratio p The threshold corresponding to 3 is 37%. Based on the judgment result, it is determined whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer;

[0071] The specific judgment method by comparing the deviation rate with the set threshold is:

[0072] When the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio are all greater than the set threshold, it is determined that the transformer to be identified is a secondary silicon steel dry-type transformer;

[0073] When two of the deviation rates of the fundamental frequency energy ratio (p1), the spectrum complexity (p2), and the high-to-low frequency energy ratio (p3) exceed a set threshold, it is determined that the transformer to be identified may use secondary silicon steel sheets.

[0074] When only one of the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-to-low frequency energy ratio exceeds the set threshold, it is determined that the transformer to be identified has a loose transformer support structure or a DC bias magnetic defect and requires further inspection.

[0075] Example 2

[0076] Based on the same inventive concept as Example 1, this embodiment proposes a secondary silicon steel sheet dry-type transformer identification system, including:

[0077] The acquisition module is used to perform no-load tests on the dry-type transformer to be identified and a new dry-type transformer of the same model and collect two noise audios emitted by the two dry-type transformers at this time;

[0078] The characteristic parameter calculation module is used to perform short-time Fourier transform analysis on the two noise audio segments to obtain the sound wave amplitude corresponding to each frequency of the two noise audio segments. The sound wave amplitude corresponding to the 50Hz multiple of the two noise audio segments is taken to calculate the fundamental frequency energy proportion, spectrum complexity and high-to-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer respectively.

[0079] The judgment module is used to calculate the deviation rate p1 of the fundamental frequency energy ratio, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio of the above-mentioned dry-type transformer to be identified and the new dry-type transformer based on the fundamental frequency energy ratio, spectrum complexity, and high-frequency and low-frequency energy ratio of the two, compare the deviation rates p1, p2, and p3 with the preset thresholds, and confirm whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result.

[0080] The acquisition module includes a condenser microphone, which is used to collect the noise emitted by the dry-type transformer during the no-load test. The condenser microphone is set at the center of the front of the dry-type transformer and is 30 cm horizontally away from the center point of the transformer core.

[0081] The following formulas are used to calculate the fundamental frequency energy proportion, spectrum complexity, and high-frequency and low-frequency energy ratio:

[0082] The expression of fundamental frequency energy ratio R is:

[0083]

[0084] in, A i for( i * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude;

[0085] The expression of spectral complexity H is:

[0086]

[0087] in, R i for( i *The energy density of 50)Hz, where n is the number of 50Hz multiples of the sound wave amplitude;

[0088] High-low frequency energy ratio The expression is:

[0089]

[0090] in, A i for( i *50)Hz corresponding to the amplitude of the sound wave, A j for( j * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude.

[0091] The deviation rate p 1. p 2 and p3. Compare with the preset threshold value and determine whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result. Specifically:

[0092] When the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio are all greater than the set threshold, it is determined that the transformer to be identified is a secondary silicon steel dry-type transformer;

[0093] When two of the deviation rates of the fundamental frequency energy ratio (p1), the spectrum complexity (p2), and the high-to-low frequency energy ratio (p3) exceed a set threshold, it is determined that the transformer to be identified may use secondary silicon steel sheets.

[0094] When only one of the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-to-low frequency energy ratio exceeds the set threshold, it is determined that the transformer to be identified has a loose transformer support structure or a DC bias magnetic defect and requires further inspection.

[0095] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the secondary silicon steel sheet dry-type transformer identification method of Example 1 is implemented.

[0096] The secondary silicon steel sheet dry-type transformer identification system and electronic device of this embodiment are based on the same inventive concept as that of Example 1. For other specific technical means, please refer to Example 1 and will not be repeated here.

[0097] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for identifying a secondary silicon steel sheet dry-type transformer, characterized in that: The following steps are involved: Perform no-load tests on the dry-type transformer to be identified and a brand new dry-type transformer of the same model, and collect two noise audios emitted by the two dry-type transformers at this time; The two noise audio segments were analyzed by short-time Fourier transform to obtain the sound wave amplitude corresponding to each frequency of the two noise audio segments. The sound wave amplitude corresponding to the 50Hz multiple of the two noise audio segments was taken to calculate the fundamental frequency energy proportion, spectrum complexity and high-to-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer respectively. According to the fundamental frequency energy ratio, spectrum complexity and high-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer, the deviation rate of the fundamental frequency energy ratio of the two is calculated. p 1. Deviation rate of spectral complexity p 2 and the deviation rate of high-low frequency energy ratio p 3. The deviation rate p 1. p 2 and p 3. Compare with the preset threshold value and determine whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result.

2. The identification method of a secondary silicon steel sheet dry-type transformer according to claim 1, characterized in that: The specific steps of collecting the two noise audios emitted by the two dry-type transformers at this time are to use a condenser microphone to collect the noise emitted by the dry-type transformers during the no-load test. The condenser microphone is set at the center of the front of the dry-type transformer and is 30 cm horizontally away from the center point of the transformer core.

3. The identification method of a secondary silicon steel sheet dry-type transformer according to claim 1, characterized in that: The following formulas are used to calculate the fundamental frequency energy proportion, spectrum complexity, and high-frequency and low-frequency energy ratio: The expression of fundamental frequency energy ratio R is: in, A i for( i * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude; The expression of spectral complexity H is: in, R i for( i *The energy density of 50)Hz, where n is the number of 50Hz multiples of the sound wave amplitude; High-low frequency energy ratio The expression is: in, A i for( i *50)Hz corresponding to the amplitude of the sound wave, A j for( j * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude.

4. The identification method of a secondary silicon steel sheet dry-type transformer according to claim 1, characterized in that: The deviation rate p 1. p 2 and p 3. Compare with the preset threshold value and determine whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result. Specifically: When the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio are all greater than the set threshold, it is determined that the transformer to be identified is a secondary silicon steel sheet dry-type transformer; When two of the deviation rates of the fundamental frequency energy ratio (p1), the spectrum complexity (p2), and the high-to-low frequency energy ratio (p3) exceed a set threshold, it is determined that the transformer to be identified may be a secondary silicon steel sheet dry-type transformer. When only one of the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-to-low frequency energy ratio exceeds the set threshold, it is determined that the transformer to be identified is not a secondary silicon steel sheet dry-type transformer.

5. An identification system for a secondary silicon steel sheet dry-type transformer, characterized in that: include: The acquisition module is used to perform no-load tests on the dry-type transformer to be identified and a new dry-type transformer of the same model and collect two noise audios emitted by the two dry-type transformers at this time; The characteristic parameter calculation module is used to perform short-time Fourier transform analysis on the two noise audio segments to obtain the sound wave amplitude corresponding to each frequency of the two noise audio segments. The sound wave amplitude corresponding to the 50Hz multiple of the two noise audio segments is taken to calculate the fundamental frequency energy proportion, spectrum complexity and high-to-low frequency energy ratio of the dry-type transformer to be identified and the new dry-type transformer respectively. The judgment module is used to calculate the deviation rate p1 of the fundamental frequency energy ratio, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio of the above-mentioned dry-type transformer to be identified and the new dry-type transformer based on the fundamental frequency energy ratio, spectrum complexity, and high-frequency and low-frequency energy ratio of the two, compare the deviation rates p1, p2, and p3 with the preset thresholds, and confirm whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result.

6. The identification system for a secondary silicon steel sheet dry-type transformer according to claim 5, characterized in that: The acquisition module includes a condenser microphone, which is used to collect the noise emitted by the dry-type transformer during the no-load test. The condenser microphone is set at the center of the front of the dry-type transformer and is 30 cm horizontally away from the center point of the transformer core.

7. The identification system for a secondary silicon steel sheet dry-type transformer according to claim 5, characterized in that: The following formulas are used to calculate the fundamental frequency energy proportion, spectrum complexity, and high-frequency and low-frequency energy ratio: The expression of fundamental frequency energy ratio R is: in, A i for( i * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude; The expression of spectral complexity H is: in, R i for( i *The energy density of 50)Hz, where n is the number of 50Hz multiples of the sound wave amplitude; High-low frequency energy ratio The expression is: in, A i for( i *50)Hz corresponding to the amplitude of the sound wave, A j for( j * is the amplitude of the sound wave corresponding to 50) Hz, and n is the number of 50 Hz multiples of the sound wave amplitude.

8. The identification system for a secondary silicon steel sheet dry-type transformer according to claim 5, characterized in that: The deviation rate p 1. p 2 and p 3. Compare with the preset threshold value and determine whether the transformer to be identified is a secondary silicon steel sheet dry-type transformer based on the judgment result. Specifically: When the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-frequency and low-frequency energy ratio are all greater than the set threshold, it is determined that the transformer to be identified is a secondary silicon steel sheet dry-type transformer; When two of the deviation rates of the fundamental frequency energy ratio (p1), the spectrum complexity (p2), and the high-to-low frequency energy ratio (p3) exceed a set threshold, it is determined that the transformer to be identified may be a secondary silicon steel sheet dry-type transformer. When only one of the deviation rate p1 of the fundamental frequency energy proportion, the deviation rate p2 of the spectrum complexity, and the deviation rate p3 of the high-to-low frequency energy ratio exceeds the set threshold, it is determined that the transformer to be identified is not a secondary silicon steel sheet dry-type transformer.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for identifying a secondary silicon steel sheet dry-type transformer according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for identifying a secondary silicon steel sheet dry-type transformer according to any one of claims 1 to 4 is implemented.

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