Diagnostics of Geoinduced Currents in High Latitude Power Systems Using Machine Learning Methods

IF 0.4 4区 物理与天体物理 Q4 PHYSICS, MULTIDISCIPLINARY
A. V. Vorobev, G. R. Vorobeva
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引用次数: 0

Abstract

It is known, that geoinduced currents (GICs) take place in spatially distributed current-carrying technical systems (main pipelines, power transmission lines and telegraph lines, railway infrastructure facilities, etc.) due to geomagnetic variations (GMVs), which rate of change in high-latitude regions is often about several hundred nT/min. Flowing through the grounded windings of power transformers of system-forming electrical circuits, extreme GICs are capable of transferring their magnetic systems into saturation mode, which, in turn, can cause a failure of the corresponding electrical systems. However, due to little knowledge of the mechanisms of the emergence and development of GIC, as well as the fragmentation and heterogeneity of the available empirical data, the problem of their predicting and diagnostics today is associated with many uncertainties and remains practically unsolved. The research based on machine learning methods examines approaches to diagnostics of gas and electric power level in the electric network ‘‘Severnyi Transit.’’ In this case, both geomagnetic data recorded by magnetic stations in the subregion (Kola Peninsula, Russia) and natural (visible) indicators of extreme geomagnetic activity are used as input parameters. Using an annual sample of more than 35 000 records as an example, it was shown that the approach to GICs diagnostics, based on multiple linear regression, provides a root mean square error (RMSE) of \(\sim\)0.122 A\({}^{2}\). The use of an artificial neural network with the ReLU activation function can slightly improve the diagnostic accuracy (RMS \(\sim\) 0.119 A\({}^{2}\)). However, the interpretability and theoretical significance of the model is significantly reduced. The application, in turn, of the Bayesian classifier to the data of optical observations of auroras showed that the posterior probability of the fact that in the north the GIC level at the Vykhodnoy station during auroras will exceed 2 A is 5.78\(\%\), while the probability of exceeding this value during auroras in the zenith and south are 10.04 and 14.93\(\%\), respectively. In the absence of auroras, the model indicates that the probability of achieving a GIC of a similar level does not exceed 0.26\(\%\), and the probability of exceeding 3 A is practically zero.

Abstract Image

利用机器学习方法诊断高纬度电力系统中的地感应电流
众所周知,地磁变化(gmv)在空间分布的载流技术系统(主要管道、输电线路和电报线路、铁路基础设施等)中会产生地感应电流(GICs),高纬度地区地磁变化的速率通常在几百nT/min左右。极端gic流经系统形成电路的电力变压器接地绕组,能够将其磁系统转移到饱和模式,从而导致相应的电气系统失效。然而,由于对GIC发生和发展的机制知之甚少,以及现有经验数据的碎片化和异质性,其预测和诊断问题目前存在许多不确定性,实际上仍未得到解决。该研究基于机器学习方法,研究了电力网络“Severnyi Transit”中天然气和电力水平的诊断方法。“在这种情况下,使用分区域(俄罗斯科拉半岛)地磁站记录的地磁数据和极端地磁活动的自然(可见)指标作为输入参数。以每年超过35000条记录的样本为例,结果表明,基于多元线性回归的gis诊断方法的均方根误差(RMSE)为\(\sim\) 0.122 a \({}^{2}\)。使用带有ReLU激活函数的人工神经网络可以略微提高诊断准确率(RMS \(\sim\) 0.119 A \({}^{2}\))。然而,该模型的可解释性和理论意义大大降低。将贝叶斯分类器应用于极光光学观测资料,结果表明,北半球Vykhodnoy站在极光期间GIC水平超过2 A的后验概率为5.78 \(\%\),而南半球和天顶极光期间超过此值的后验概率分别为10.04和14.93 \(\%\)。在没有极光的情况下,该模型表明,达到类似水平的GIC的概率不超过0.26 \(\%\),超过3a的概率几乎为零。
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来源期刊
Moscow University Physics Bulletin
Moscow University Physics Bulletin PHYSICS, MULTIDISCIPLINARY-
CiteScore
0.70
自引率
0.00%
发文量
129
审稿时长
6-12 weeks
期刊介绍: Moscow University Physics Bulletin publishes original papers (reviews, articles, and brief communications) in the following fields of experimental and theoretical physics: theoretical and mathematical physics; physics of nuclei and elementary particles; radiophysics, electronics, acoustics; optics and spectroscopy; laser physics; condensed matter physics; chemical physics, physical kinetics, and plasma physics; biophysics and medical physics; astronomy, astrophysics, and cosmology; physics of the Earth’s, atmosphere, and hydrosphere.
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