An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology
In order to find transformer (reactor) faults in time, a transformer working condition detection method and verification system based on voiceprint recognition technology was proposed. In this system, 73 groups of transformer audio were collected by the voice sensor on-site, with a total of about 18...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7712649 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849408168074936320 |
|---|---|
| author | Litan Cao Huabing Wei Zhi Huang Minglei Shi |
| author_facet | Litan Cao Huabing Wei Zhi Huang Minglei Shi |
| author_sort | Litan Cao |
| collection | DOAJ |
| description | In order to find transformer (reactor) faults in time, a transformer working condition detection method and verification system based on voiceprint recognition technology was proposed. In this system, 73 groups of transformer audio were collected by the voice sensor on-site, with a total of about 1800 min. The recognition pattern based on a deep learning convolutional neural network was established. Through experiments, it was found that aiming at the additive superposition problem of transformer sound generated by a stable working condition and unstable instantaneous noise, a new method based on the cosine similarity algorithm was proposed to realize the separation detection of sound pattern superposition. The acoustic signals of the iron core under sinusoidal excitation were mainly frequency components of 100 Hz and 200 Hz. Harmonic excitation would aggravate the noise in this frequency band, and the third harmonic excitation had the greatest influence. Due to DC magnetic bias, the hysteresis loop of the iron core was distorted to a certain pole. In addition to the 100 Hz component, the odd harmonics of 150 Hz, 250 Hz, and 350 Hz and even harmonics of 200 Hz, 300 Hz, and 400 Hz also increased obviously. With the increase of direct current content, the performance of the noise signal became more prominent. A transformer working condition detection and verification analysis system was established. |
| format | Article |
| id | doaj-art-3d79cb1431684e3083d1f0dce124e437 |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-3d79cb1431684e3083d1f0dce124e4372025-08-20T03:35:51ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/7712649An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition TechnologyLitan Cao0Huabing Wei1Zhi Huang2Minglei Shi3EHV Branch Company of State Grid Zhejiang Electric Power Co., LtdEHV Branch Company of State Grid Zhejiang Electric Power Co., LtdEHV Branch Company of State Grid Zhejiang Electric Power Co., LtdEHV Branch Company of State Grid Zhejiang Electric Power Co., LtdIn order to find transformer (reactor) faults in time, a transformer working condition detection method and verification system based on voiceprint recognition technology was proposed. In this system, 73 groups of transformer audio were collected by the voice sensor on-site, with a total of about 1800 min. The recognition pattern based on a deep learning convolutional neural network was established. Through experiments, it was found that aiming at the additive superposition problem of transformer sound generated by a stable working condition and unstable instantaneous noise, a new method based on the cosine similarity algorithm was proposed to realize the separation detection of sound pattern superposition. The acoustic signals of the iron core under sinusoidal excitation were mainly frequency components of 100 Hz and 200 Hz. Harmonic excitation would aggravate the noise in this frequency band, and the third harmonic excitation had the greatest influence. Due to DC magnetic bias, the hysteresis loop of the iron core was distorted to a certain pole. In addition to the 100 Hz component, the odd harmonics of 150 Hz, 250 Hz, and 350 Hz and even harmonics of 200 Hz, 300 Hz, and 400 Hz also increased obviously. With the increase of direct current content, the performance of the noise signal became more prominent. A transformer working condition detection and verification analysis system was established.http://dx.doi.org/10.1155/2022/7712649 |
| spellingShingle | Litan Cao Huabing Wei Zhi Huang Minglei Shi An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology Journal of Control Science and Engineering |
| title | An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology |
| title_full | An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology |
| title_fullStr | An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology |
| title_full_unstemmed | An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology |
| title_short | An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology |
| title_sort | operating status analysis system of reactor equipment based on voiceprint recognition technology |
| url | http://dx.doi.org/10.1155/2022/7712649 |
| work_keys_str_mv | AT litancao anoperatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology AT huabingwei anoperatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology AT zhihuang anoperatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology AT mingleishi anoperatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology AT litancao operatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology AT huabingwei operatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology AT zhihuang operatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology AT mingleishi operatingstatusanalysissystemofreactorequipmentbasedonvoiceprintrecognitiontechnology |