Spectral analysis of geomagnetically induced current and local magnetic field during the 17 March 2013 geomagnetic storm

Xu, Wenhao; Zou, Xing; Balan, N.; Liang, Likai; Wang, Yanling; Zhang, Qinghe; Sun, Zidan; Li, Wenbin

doi:10.1016/j.asr.2022.02.025

Cited by 7 publications

(12 citation statements)

References 37 publications

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“…It was observed that high energy wavelet coefficients of 𝑑𝐵 𝑥 𝑑𝑡 ⁄ correlated well with GIC. Similar to Falayi et al, another study applies Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) to GIC and 𝑑𝐵 𝑥 𝑑𝑡 ⁄ showing very similar spectrograms [31]. The wavelet coefficient of 𝑑𝐵 𝑥 𝑑𝑡 ⁄ and GIC are very similar indicating strong correlation between them.…”

Section: A Frequency Components Of Gicsmentioning

confidence: 64%

Geomagnetically Induced Currents: Frequency Spectra and Threats to Voltage Stability

et al. 2022

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Geomagnetically Induced Currents (GICs) are induced after a complex interaction between the sun and earth's magnetic field. GICs may cripple power systems leading to voltage collapse and a drop in power quality. While the commonly-used dc model for GICs is valid for steady-state analyses of power systems and transformer responses, GICs must be represented with their continuously varying magnitudes and frequency components to understand the dynamic power system's response to solar storms. In this paper, we used measured GIC data from the 2003 Halloween storm to compare several signal processing techniques. The Fast Fourier Transform (FFT) with a fixed window size, Short-Time Fourier Transform (STFT) with a variable window size, wavelet transform, and Particle-Swarm Optimization (PSO) methods were used to identify the GIC frequency characteristics. To understand the effects of these GIC characteristics on voltage stability, a multi-machine network was modelled on a digital real-time simulator (OPAL-RT). The results reveal the limitations of fixed window FFT in capturing the dominant high-frequency GIC bandwidth arising during peak solar activity. Wavelet and STFT are recommended for GIC data analysis as they provide timefrequency localization. High-frequency GICs during sudden storm commencements and pulsation activity are more of a concern for voltage stability than lower frequency components. Novel results from this study suggest that limiting contingency analysis to only high magnitudes of dc/GIC does not cater for the underlining GIC dynamics. In addition to magnitude thresholds, contingency analysis should incorporate GIC characteristics such as frequency which affect voltage dynamics and thus power system stability.

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Section: A Frequency Components Of Gicsmentioning

confidence: 64%

Geomagnetically Induced Currents: Frequency Spectra and Threats to Voltage Stability

et al. 2022

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“…CWT is a widely spread spectral analysis method for nonstationary signals, in particular GICs [31][32][33][34]. In the case of intellectual fault diagnosis for power systems, CWT is often used as a feature extraction method from different transformer signals such as vibrations, currents and voltages [14][15][16].…”

Section: Cwtmentioning

confidence: 99%

A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms

Aksenovich,

Selivanov

2024

Applied Sciences

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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.

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“…In this context, the computational speed can be neglected in favor of greater accuracy of the determination of the wavelet coefficients values. The Morlet wavelet was also previously used by other researchers to calculate the GIC wavelet spectra [Falayi et al, 2017;Xu et al, 2022]. The rest of the mother wavelets in Figure 2 are not useful for the purpose of the study because of blurry localization of the coefficients in the time (Figure 2b, Figure 2e, Figure 2g) and frequency (Figure 2c) domains and the presence of artifacts (Figure 2f), which do not provide a clear picture of the spectral characteristics of the processes in the neutral current.…”

Section: Wavelet Transform Parametersmentioning

confidence: 99%

“…In recent times, researchers involved in the study of geomagnetically induced currents began to apply spectral analysis techniques more frequently [Adhikari et al, 2019;Falayi et al, 2017;Xu et al, 2022]. The wavelet analysis method is the most commonly utilized method for spectral processing of quasi-DC currents.…”

Section: Introductionmentioning

confidence: 99%

“…There is no information about the used analyzing function. Besides that, authors in [Xu et al, 2022] used spectra of CWT to gain greater insight into a relation between GIC and horizontal component of geomagnetic field dBx/dt during the intense geomagnetic storm. The research showed that GIC and dBx/dt data have a certain correlation between them.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet analysis of geomagnetically induced currents during the strong geomagnetic storms

Aksenovich

Bilin

Saharov

et al. 2022

Russian Journal of Earth Sciences

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The main problem of electric utilities around the world is to ensure continuous power supply to consumers. One of the causes of power outages and blackouts can be geomagnetic storms during periods of the increased solar activity. They arouse geomagnetically induced currents (GICs) flowing in the long-distance high-voltage power grids on Earth’s surface. The history of this phenomenon investigation shows that GICs during strong geomagnetic storms had led to blackouts in certain regions of Canada, Sweden and the USA. To study these phenomena and assess the risks of such accidents for the regional system, a GICs registration system in 330 kV autotransformers neutrals of the Kola-Karelian power transit was developed in northwestern Russia. During 11 years of monitoring numerous cases of the flow of high values of quasi-dc currents with different time durations, induced by variations of the geomagnetic field, have been registered. In order to analyze the currents a wavelet transform was chosen, since this method allows to define not only the frequency composition but also changes in spectral characteristics over time, which is significant in the study of GIC. The paper presents a discussion of GIC scalograms obtained for four events of Solar Cycle 24: 13-14 November 2012, 17-18 March 2015, 7-8 September 2015 and 7-8 September 2017. The analysis showed that the characteristic duration of the peak of the considered GICs is from 4.6 to 11.1 min.

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Spectral analysis of geomagnetically induced current and local magnetic field during the 17 March 2013 geomagnetic storm

Cited by 7 publications

References 37 publications

Geomagnetically Induced Currents: Frequency Spectra and Threats to Voltage Stability

Geomagnetically Induced Currents: Frequency Spectra and Threats to Voltage Stability

A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms

Wavelet analysis of geomagnetically induced currents during the strong geomagnetic storms

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