A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms
During geomagnetic storms, which are a result of solar wind’s interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth’s surface. It causes a number of negative phenomena that affect the normal operat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/895 |
| _version_ | 1797340114666389504 |
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| author | Tatyana Aksenovich Vasiliy Selivanov |
| author_facet | Tatyana Aksenovich Vasiliy Selivanov |
| author_sort | Tatyana Aksenovich |
| collection | DOAJ |
| description | During geomagnetic storms, which are a result of solar wind’s interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth’s surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its effects on transformers, a GIC monitoring system was created in 2011. The system consists of devices that are installed in the neutrals of autotransformers at five substations of the Kola–Karelian power transit in northwestern Russia. Considering the significant amount of data accumulated over 12 years of operating the GIC monitoring system, manual analysis becomes impractical. To analyze the constantly growing volume of recorded data effectively, a method for the automatic classification of GICs in autotransformer neutrals was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. The classifier’s performance is evaluated using accuracy and binary cross-entropy loss metrics. As the result of comparing four CNN architectures, a model that showed high GIC classification performance on the validation set was chosen as the final model. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer. |
| first_indexed | 2024-03-08T09:58:18Z |
| format | Article |
| id | doaj.art-ea7b78c01fd54ed7b4929c6ea3822505 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:58:18Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ea7b78c01fd54ed7b4929c6ea38225052024-01-29T13:45:41ZengMDPI AGApplied Sciences2076-34172024-01-0114289510.3390/app14020895A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current ScalogramsTatyana Aksenovich0Vasiliy Selivanov1Northern Energetics Research Centre—Branch of the Federal Research Centre “Kola Science Centre of the Russian Academy of Sciences” (NERC KSC RAS), 184209 Apatity, RussiaNorthern Energetics Research Centre—Branch of the Federal Research Centre “Kola Science Centre of the Russian Academy of Sciences” (NERC KSC RAS), 184209 Apatity, RussiaDuring geomagnetic storms, which are a result of solar wind’s interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth’s surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its effects on transformers, a GIC monitoring system was created in 2011. The system consists of devices that are installed in the neutrals of autotransformers at five substations of the Kola–Karelian power transit in northwestern Russia. Considering the significant amount of data accumulated over 12 years of operating the GIC monitoring system, manual analysis becomes impractical. To analyze the constantly growing volume of recorded data effectively, a method for the automatic classification of GICs in autotransformer neutrals was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. The classifier’s performance is evaluated using accuracy and binary cross-entropy loss metrics. As the result of comparing four CNN architectures, a model that showed high GIC classification performance on the validation set was chosen as the final model. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer.https://www.mdpi.com/2076-3417/14/2/895geomagnetically induced currentsautotransformercontinuous wavelet transformconvolutional neural networkbinary classification |
| spellingShingle | Tatyana Aksenovich Vasiliy Selivanov A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms Applied Sciences geomagnetically induced currents autotransformer continuous wavelet transform convolutional neural network binary classification |
| title | A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms |
| title_full | A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms |
| title_fullStr | A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms |
| title_full_unstemmed | A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms |
| title_short | A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms |
| title_sort | deep learning approach for the automated classification of geomagnetically induced current scalograms |
| topic | geomagnetically induced currents autotransformer continuous wavelet transform convolutional neural network binary classification |
| url | https://www.mdpi.com/2076-3417/14/2/895 |
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