Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks
Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/5/2382 |
| _version_ | 1797615417499320320 |
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| author | Aniket Vatsa Ananda Shankar Hati Vadim Bolshev Alexander Vinogradov Vladimir Panchenko Prasun Chakrabarti |
| author_facet | Aniket Vatsa Ananda Shankar Hati Vadim Bolshev Alexander Vinogradov Vladimir Panchenko Prasun Chakrabarti |
| author_sort | Aniket Vatsa |
| collection | DOAJ |
| description | Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet. |
| first_indexed | 2024-03-11T07:26:01Z |
| format | Article |
| id | doaj.art-f0dd60e56a9b4325bb6facec7a5cfc16 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T07:26:01Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f0dd60e56a9b4325bb6facec7a5cfc162023-11-17T07:37:54ZengMDPI AGEnergies1996-10732023-03-01165238210.3390/en16052382Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory NetworksAniket Vatsa0Ananda Shankar Hati1Vadim Bolshev2Alexander Vinogradov3Vladimir Panchenko4Prasun Chakrabarti5Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, IndiaDepartment of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, IndiaLaboratory of Power Supply and Heat Supply, Federal Scientific Agroengineering Centre VIM, 109428 Moscow, RussiaLaboratory of Power Supply and Heat Supply, Federal Scientific Agroengineering Centre VIM, 109428 Moscow, RussiaDepartment of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, RussiaDepartment of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, IndiaPower transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.https://www.mdpi.com/1996-1073/16/5/2382power transformeroil-immersed insulationmoisture forecastinglong short-term memory |
| spellingShingle | Aniket Vatsa Ananda Shankar Hati Vadim Bolshev Alexander Vinogradov Vladimir Panchenko Prasun Chakrabarti Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks Energies power transformer oil-immersed insulation moisture forecasting long short-term memory |
| title | Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks |
| title_full | Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks |
| title_fullStr | Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks |
| title_full_unstemmed | Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks |
| title_short | Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks |
| title_sort | deep learning based transformer moisture diagnostics using long short term memory networks |
| topic | power transformer oil-immersed insulation moisture forecasting long short-term memory |
| url | https://www.mdpi.com/1996-1073/16/5/2382 |
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