Aksenovich, T.; Selivanov, V. A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms. Appl. Sci.2024, 14, 895.
Aksenovich, T.; Selivanov, V. A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms. Appl. Sci. 2024, 14, 895.
Aksenovich, T.; Selivanov, V. A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms. Appl. Sci.2024, 14, 895.
Aksenovich, T.; Selivanov, V. A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms. Appl. Sci. 2024, 14, 895.
Abstract
During geomagnetic storms, which are a result of solar wind interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long-distance high-voltage power grids 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 possible effects on transformers, a GIC monitoring system was created in 2011, the devices of which were installed at five substations of the Kola-Karelian power transit in northwestern Russia. Over 12 years of operating the system a large amount of data has been accumulated, which cannot be analyzed manually within a reasonable amount of time. To analyze the constantly growing volume of recorded data effectively, a method for automatic classification of GIC in autotransformer neutral 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. As the result of comparing four CNNs with different architectures, a model that showed excellent GIC classification performance on the validation set (100.00% accuracy and loss of 0.0115) was chosen.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
