An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data
The geomagnetically induced currents (GICs) in extended grounded technological systems are driven by telluric electric fields induced by the rapid changes of the geomagnetic field. The paper is concerned with research on the approach to diagnostics of GIC in the power transmission lines in northwest...
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/3/1522 |
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| author | Andrei Vorobev Anatoly Soloviev Vyacheslav Pilipenko Gulnara Vorobeva Yaroslav Sakharov |
| author_facet | Andrei Vorobev Anatoly Soloviev Vyacheslav Pilipenko Gulnara Vorobeva Yaroslav Sakharov |
| author_sort | Andrei Vorobev |
| collection | DOAJ |
| description | The geomagnetically induced currents (GICs) in extended grounded technological systems are driven by telluric electric fields induced by the rapid changes of the geomagnetic field. The paper is concerned with research on the approach to diagnostics of GIC in the power transmission lines in northwestern Russia based on data from IMAGE magnetometers. Based on the results of the statistical and correlation analysis of the objective function (the level of the GIC recorded at the Vykhodnoy transformer station) and geomagnetic data recorded by the nearby IMAGE magnetometers, the features that best characterize the target variable in a given region are distinguished. Using machine learning (ML) methods, the defined number of feature objects is used to develop the relationship for the GIC diagnostics. Evaluation of the coefficient of determination for a stack of various ML methods revealed that the regression approach and artificial neural networks (ANN) are the best solution for the problem under consideration. Verification tests have shown that ANN-based approach and regression methods provide nearly the same diagnostic accuracy for GIC (the mean square error 0.12 A<sup>2</sup>). However, ANN-based methods are less interpretable and require more computer resources. |
| first_indexed | 2024-03-10T00:11:21Z |
| format | Article |
| id | doaj.art-a73c8848914944e6b90b03f2b4c36b5e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:11:21Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a73c8848914944e6b90b03f2b4c36b5e2023-11-23T15:59:02ZengMDPI AGApplied Sciences2076-34172022-01-01123152210.3390/app12031522An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers DataAndrei Vorobev0Anatoly Soloviev1Vyacheslav Pilipenko2Gulnara Vorobeva3Yaroslav Sakharov4Geophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaGeophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaGeophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaFaculty of Informatics and Robotics, Ufa State Aviation Technical University, 450008 Ufa, RussiaPolar Geophysical Institute, 184209 Apatity, RussiaThe geomagnetically induced currents (GICs) in extended grounded technological systems are driven by telluric electric fields induced by the rapid changes of the geomagnetic field. The paper is concerned with research on the approach to diagnostics of GIC in the power transmission lines in northwestern Russia based on data from IMAGE magnetometers. Based on the results of the statistical and correlation analysis of the objective function (the level of the GIC recorded at the Vykhodnoy transformer station) and geomagnetic data recorded by the nearby IMAGE magnetometers, the features that best characterize the target variable in a given region are distinguished. Using machine learning (ML) methods, the defined number of feature objects is used to develop the relationship for the GIC diagnostics. Evaluation of the coefficient of determination for a stack of various ML methods revealed that the regression approach and artificial neural networks (ANN) are the best solution for the problem under consideration. Verification tests have shown that ANN-based approach and regression methods provide nearly the same diagnostic accuracy for GIC (the mean square error 0.12 A<sup>2</sup>). However, ANN-based methods are less interpretable and require more computer resources.https://www.mdpi.com/2076-3417/12/3/1522geomagnetically induced currentsgeomagnetic fieldgeomagnetic variationsmachine learning |
| spellingShingle | Andrei Vorobev Anatoly Soloviev Vyacheslav Pilipenko Gulnara Vorobeva Yaroslav Sakharov An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data Applied Sciences geomagnetically induced currents geomagnetic field geomagnetic variations machine learning |
| title | An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data |
| title_full | An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data |
| title_fullStr | An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data |
| title_full_unstemmed | An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data |
| title_short | An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data |
| title_sort | approach to diagnostics of geomagnetically induced currents based on ground magnetometers data |
| topic | geomagnetically induced currents geomagnetic field geomagnetic variations machine learning |
| url | https://www.mdpi.com/2076-3417/12/3/1522 |
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