Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm
The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. Th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/15/4332 |
| _version_ | 1797560376588500992 |
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| author | Daniel Jancarczyk Marcin Bernaś Tomasz Boczar |
| author_facet | Daniel Jancarczyk Marcin Bernaś Tomasz Boczar |
| author_sort | Daniel Jancarczyk |
| collection | DOAJ |
| description | The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task. |
| first_indexed | 2024-03-10T17:59:06Z |
| format | Article |
| id | doaj.art-0d5ef90113c04d1b9fe42e951bf39250 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:59:06Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0d5ef90113c04d1b9fe42e951bf392502023-11-20T08:59:23ZengMDPI AGSensors1424-82202020-08-012015433210.3390/s20154332Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary AlgorithmDaniel Jancarczyk0Marcin Bernaś1Tomasz Boczar2Department of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, PolandDepartment of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, PolandInstitute of Electric Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, PolandThe paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.https://www.mdpi.com/1424-8220/20/15/4332low-frequency sensorpower transformermachine learninglow-frequency noisegenetic algorithm |
| spellingShingle | Daniel Jancarczyk Marcin Bernaś Tomasz Boczar Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm Sensors low-frequency sensor power transformer machine learning low-frequency noise genetic algorithm |
| title | Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm |
| title_full | Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm |
| title_fullStr | Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm |
| title_full_unstemmed | Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm |
| title_short | Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm |
| title_sort | distribution transformer parameters detection based on low frequency noise machine learning methods and evolutionary algorithm |
| topic | low-frequency sensor power transformer machine learning low-frequency noise genetic algorithm |
| url | https://www.mdpi.com/1424-8220/20/15/4332 |
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