GIC due to storm sudden commencement in low-latitude high ... - NSFC

Space Weather RESEARCH ARTICLE 10.1002/2015SW001263 Key Points: • GICs at two low-latitude substations are observed during the large storm of 17 March 2015 • GICs due to SSC are 2–3 times higher than those due to storm main phase • The GICs caused by the SSC at the two substations are reproduced by a global MHD model

Correspondence to: J. J. Zhang, [email protected]

Citation: Zhang, J. J., C. Wang, T. R. Sun, C. M. Liu, and K. R. Wang (2015), GIC due to storm sudden commencement in low-latitude high-voltage power network in China: Observation and simulation, Space Weather, 13, doi:10.1002/2015SW001263.

Received 8 JUL 2015 Accepted 8 SEP 2015 Accepted article online 14 SEP 2015

©2015. American Geophysical Union. All Rights Reserved.

ZHANG ET AL.

GIC due to storm sudden commencement in low-latitude high-voltage power network in China: Observation and simulation J. J. Zhang1 , C. Wang1 , T. R. Sun1 , C. M. Liu2 , and K. R. Wang2 1 State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, China, 2 School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China

Abstract The impact of geomagnetically induced currents (GICs) on the power networks at middle and low latitudes has attracted attention in recent years with the increase of large-scale power networks. In this study, we report the GIC monitored at two low-latitude 500 kV substations of China during the large storm of 17 March 2015. The GIC due to the storm sudden commencement (SSC) was much higher than that during the storm main phase. This phenomenon is more likely to happen at low-latitude locations, highlighting the importance of SSC in inducing GIC in low-latitude power networks. Furthermore, we ran a global MHD model to simulate the GIC during this SSC event by using the solar wind observation as input. The model results reproduced the main features of the GIC. The study also indicated that the eastward component of the geoelectric field is dominant for low-latitude locations during the SSC events. Further, topology and electrical parameters of the power grids make significant differences in the GIC levels. 1. Introduction Geomagnetically induced currents (GICs) in long-distance conductive ground infrastructures such as pipelines, communication cables, and high-voltage power transmission systems are harmful space weather effects to human society [e.g., Boteler et al., 1998], and the problem has attracted elevated attention over recent several years especially in terms of adverse effect on increasingly large scale power networks. It is well known that the primary cause of GIC is the interaction between solar wind and the geomagnetic field, which induces geoelectric field on the Earth’s surface, producing GIC in the transmission lines. Large GICs are usually observed at high-latitude locations due to large magnitude and high rates of change of the geomagnetic field disturbance caused by intensification of ionospheric electrojets in association with substorms [Boteler, 2001; Lam et al., 2002; Wik et al., 2008; Pulkkinen et al., 2008]; thus, GIC had been considered as a particular high-latitude problem for a long time [Pirjola, 2005]. However, there were reports showing that GIC can disrupt the power networks at lower latitudes in extreme events; for example, Gaunt and Coetzee [2007] reported that some transformer failures were detected in South Africa after the Halloween storm of 2003. In order to transport a significant amount of electricity over greater distances, the trend has been to build long transmission lines which are exposed to larger induced voltages driving larger GIC [Molinski, 2002]; this makes the power networks at lower latitudes more vulnerable to GIC attacks. To examine the GIC effect, observations and/or modeling works have been conducted in many lower latitude countries, for instance, China, Japan, Australia, New Zealand, Brazil, and South Africa [Trivedi et al., 2007; Liu et al., 2009; Watari et al., 2009; Marshall et al., 2011, 2012; Barbosa et al., 2015; Matandirotya et al., 2015]. It has been well established that the main phase of a geomagnetic storm, which is associated with ring current intensifications is a cause of large GIC and hence a risk factor for power systems at middle and low latitudes [Kappenman, 2006; Gaunt and Coetzee, 2007; Trivedi et al., 2007; Liu et al., 2009; Torta et al., 2012; Barbosa et al., 2015]. However, some disruptions of the power networks at lower latitudes were caused by geomagnetic sudden impulse (SI) or storm sudden commencement (SSC) which is defined as a specific kind of SI that is followed by a storm. Kappenman [2003] reported that the magnetospheric shock associated with SSC had even caused some power network faults in America; for example, an SSC that occurred on 15 July 2000 storm triggered simultaneous tripping of capacitor banks at two substations in the Tennessee Valley Authority power grid and the 31 March 2001 SSC onset caused the tripping of the East Fishkill capacitor bank. The author suggested that the SSCs or SIs are an important source for large GIC that occur at middle-latitude GIC DUE TO SSC

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locations and can cause power system disruption comparable to those commonly observed at high latitudes. Béland and Small [2004] reported that a transformer fault occurred on New Zealand’s South Island at 01:53 UT during the geomagnetic storm of 6 November 2001, coincident with an SSC at 01:51 UT, which was caused by an increase of solar wind dynamic pressure up to nearly 20 nPa. After comparing that event with GIC activity caused by the 2003 Halloween storm, Marshall et al. [2012] pointed out that solar wind shocks and associated SI events might be as hazardous to middle-latitude power networks as GIC activity occurring during the main phase of large storms. Though located mainly at middle and low latitudes, the GIC problem is taken seriously in China nowadays as the increasing scale of the Chinese high-voltage power networks makes the networks more likely to suffer the GIC hazards. Supported by the National High Technology Research and Development Program (863 Program for short), a multipoint GIC-monitoring campaign was launched and the project came into service at the end of 2014. The campaign recorded the GIC signal during the strongest geomagnetic storm of the current solar cycle, which struck on 17 March 2015. And the unique feature of the records is that the GIC caused by the SSC prior to the storm is more remarkable than that during the main phase, which will be described in detail next. This emphasizes the importance of the SSC in producing GIC at low latitudes and makes the prediction of GIC caused by SSC events at these locations particularly necessary. To predict the GIC impact at high-latitude power networks, empirical models and global magnetohydrodynamics (MHD) models have been used to model the GIC or proxies of GIC by using the information of the upstream solar wind as input [Weigel et al., 2003; Wintoft, 2005; Raeder et al., 2001; Pulkkinen et al., 2007; Yu and Ridley, 2008; Pulkkinen et al., 2010; Zhang et al., 2012]. And now the work comes to the step of transitioning the scientific capability into operational utilization; toward this end, Pulkkinen et al. [2013] selected five models at the Community Coordinated Modeling Center and tested the model’s capability to reproduce observed geomagnetic perturbations at 12 high-latitude geomagnetic observatories during six geomagnetic events. However, few predictions of GIC at middle- and low-latitude locations from the upstream solar wind conditions have been conducted. Ngwira et al. [2013] simulated the induced geoelectric field at specific ground-based active INTERMAGNET magnetometer sites, including some middle- and low-latitude sites by using the Space Weather Modeling Framework developed at the University of Michigan under the assumption that the 23 July 2012 extreme space weather event was Earth directed. In their study, the resistive Quebec ground conductivity model was adopted for all of the magnetometer sites to give a rough estimate; the work did not involve further calculation of the GIC at power networks. Now it is time to put the prediction of GIC at specific power network at lower latitudes on the agenda since recent reports highlighted the effect of GIC there; moreover, giving the GIC value directly for the specific power network would be more useful for the end decision-making users. In this study we will present the GIC signals caused by the large storm of 17 March 2015 recorded at two Chinese low-latitude substations and compare the signal caused by the SSC prior to the storm and other phases. The geomagnetic observations at six Chinese magnetometer sites are used to deduce the GIC index and to analyze the evolution of GIC activity from higher- to lower-latitude locations. In order to predict the GIC caused by the SSC event, we run a global MHD code to simulate the GIC signal recorded at the two substations with the solar wind observations as input. The forecasting results and the real GIC recordings are compared to estimate the model’s performance.

2. Observations The largest geomagnetic storm of the 24th solar cycle so far broke out on 17 March 2015. It was rated G4 intensity on the five-level NOAA space weather scale. The AE and SYM-H indices during the big storm are shown in Figure 1; the SSC started at 04:44 UT as the sudden increase of the SYM-H index by 50 nT; after that the index fluctuated and dove below −200 nT at around 23:00 UT. During the storm, the aurora activity was intense and very active auroras were seen even at surprisingly low latitudes with the minimum of AL index approached −2500 nT. As the Washington Post reported, auroras were seen across Alaska, Canada, and many states in the lower continental United States such as Tennessee, New Mexico, and Oklahoma. GIC-monitoring equipment installed at two low-latitude substations in China started operation in the end of 2014 and recorded the GIC due to this large storm. The two substations are Shuanglong 500 kV substation in the Zhejiang province (geographic latitude ∼29∘ , geomagnetic latitude ∼19∘ ) and Lili 500 kV substation in the Fujian province (geographic latitude ∼26∘ , geomagnetic latitude ∼16∘ ). The locations of the two substations ZHANG ET AL.

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Figure 1. AU/AL index and SYM-H index during the big storm of 17 March 2015.

are labeled on the map of Figure 2 along with six geomagnetic observatories. The observed GIC at the two substations are show in Figure 3; vertical red dot line indicates the start of the SSC; SYM-H index is also show in the figure to give a clear view of the corresponding phase of the storm. Although the GIC observed at the two sites are not very high (discussed in section 5), Figure 3 shows the GIC signals at both of the two substations appear as spikes due to the SSC, and the unique feature is the spikes are much larger than that during other phases of the storm. At the Shuanglong 500 kV substation, GIC caused by the SSC is 4.5 A and it is about 3 times of the peak value during the storm main phase; GIC value of 2.8 A due to the SSC is 2 times that during main phase at the Lili 500 kV substation. Previously, during a storm of similar intensity a GIC due to a combined SSC event and main storm phase was documented at Fukumitsu substation (geographic latitude ∼34∘ , geomagnetic latitude ∼26∘ ) in central Japan. In that event a peak GIC of over 40 A due to an

Figure 2. The locations of the two low-latitude substations of China (Shuanglong 500 kV substation and Lili 500 kV substation) and six geomagnetic observatories in geographic coordinates; from north to south the geomagnetic observatories are MHT, BMT, MLS, HZT, QZH, and ZQT.

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Figure 3. The GIC measured at (a) Shuanglong 500 kV substation and (b) Lili 500 kV substation during the large storm of 17 March 2015; (c) SYM-H index is also plotted to show the corresponding storm phase of the GIC signals. Vertical red dot line denotes the SSC event.

SSC event on 6 November 2001 was observed, which was nearly equal to the GIC caused by equatorial ring current intensifications in the several hours period after the SSC onset, as shown in Figure 4 of the paper of Kappenman [2003]. The two Chinese substations are located at lower latitudes than the Fukumitsu substation; thus, the GIC reported here suggests that the GIC due to the SSC can be even stronger than that during storm main phase, implying the importance of the SSC in producing GIC in low-latitude power networks. The SSC at these low-latitude locations are mainly caused by the magnetopause current; one should keep in mind that the magnetic signature of SSC can be amplified by the equatorial electrojet at the equatorial region, which increases the region’s susceptibility to GIC [Carter et al., 2015]. To investigate the variation feature of the GIC activity from higher latitude to lower latitude, we check the geomagnetic variations recorded at six geomagnetic observatories in China and deduce the GIC index at each site during the large storm. The GIC index is a proxy for geoelectric field, representing the GIC activity level. The method to deduce the index involves utilizing a frequency domain filter as √ Z(f ) =

f i 𝜋4 e fN

(1)

where f is frequency and fN is the Nyquist frequency. Typically, the 1 min sampled geomagnetic field data is transformed into the frequency domain, then after filtering, the signal is reverse transformed. The x (north-south) and y (east-west) component of geomagnetic field variation are used to calculate GICy and GICx index, respectively, using detailed method for calculating the index in Marshall et al. [2011]. Only GICy index is shown here because the GICx index is typically lower at low latitudes. The x component (north-south component in geographic coordinates) of the geomagnetic perturbations at six Chinese magnetometer sites are presented in Figure 4a. The six magnetometer sites are Mohe station (MHT), Beijing Ming Tomb station (BMT), Malingshan station (MLS), Hangzhou station (HZT), Quanzhou station (QZH), and Zhaoqing station (ZQT); ZHANG ET AL.

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Figure 4. (a) The x component of geomagnetic field variations at six Chinese magnetometer sites from north to south; (b) the GICy index at corresponding sites; the vertical red dot lines in the figures denote the SSC.

their locations can be found on the map in Figure 2. The BMT data are obtained from the INTERMAGNET, the data of QZH are obtained from Geomagnetic Network of China, and the other data are provided by the Chinese Meridian Project [Wang, 2010]. From the top to bottom, Figure 4a shows the recordings of the sites located from north to south with the vertical red dotted line denoting the SSC event. As shown in the figure, the six curves are similar to each other, while the amplitude of SSC increasing from 36 nT at MHT (the northernmost station) to 69 nT at ZQT (the southernmost station). Based on the x component of the geomagnetic perturbations, we calculate the GICy index at each site and present them in Figure 4b. With a close-up view of the GICy index from MHT to ZQT, we can see that the index associated with the SSC increases from the station MHT to ZQT, while the index associated with the storm main phase decreases slightly from north to south, the index due to the SSC becomes larger than that during the main phase as the decrease of the latitude to low latitude. This is consistent with the features of GIC signals observed at the two low-latitude substations Shuanglong and Lili. The comparative analysis of the GICy index at the six sites implies the phenomenon that SSC-caused GIC is dominating the GIC during storm is more likely to happen at low-latitude locations.

3. Parameters Determination The above data and analysis indicate that the SSC is one of the more important sources for GIC that occurs at low-latitude power networks; thus, the prediction of this kind of GIC becomes necessary. In order to model the GIC due to the SSC event of 17 March 2015 at the two Chinese GIC-monitoring sites, some parameters of the power grids are determined in this section. Generally speaking, calculation of GIC in the network can be performed in two independent steps: the geophysical step and the engineering step. The geophysical step is to determine the geoelectric field in the area of the network under study; the engineering step obtains the induced currents due to the given electric field. Based on the plane wave method [Cagniard, 1953], the geoelectric field components Ex,y can be computed in terms of the perpendicular geomagnetic field components By,x as t

1 dBy,x (u) 1 du Ex,y = ± √ √ ∫ 𝜋𝜇0 𝜎 −∞ t − u dt

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(2)

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Figure 5. The x and y components of the geomagnetic variation observed at HZT, the x and y components of geoelectric field, and the measured (black curve) and fitted (red curve) GIC at Shuanglong 500 kV substation during the SSC event of 17 March 2015.

where 𝜇0 is the vacuum permeability, 𝜎 is the conductivity of the uniform ground, and the subscripts x and y denote the north and the east in geographic coordinates, respectively. After obtaining the electric field, the GIC can be calculated straightforwardly from equation (3) if we take the simplest assumption that the electric field is spatially uniform over the region of the system: GIC(t) = aEx (t) + bEy (t)

(3)

where a and b are constant coefficients, which are determined by the topology and electrical properties of the system of interest. Theoretically, the parameters a and b can be obtained by knowing the network topology, station coordinates, transformer resistances, transmission line resistances, and station earthing resistances [Lehtinen and Pirjola, 1985]; they also can be deduced empirically by fitting the electric field and the corresponding GIC [Pulkkinen et al., 2006; Wik et al., 2008; Zhang et al., 2012]. In this study, we use the empirical method to determine the parameters a and b for the two substations. First, the geomagnetic variation recorded at the nearest magnetometer site is used to calculate the induced geoelectric field; here geomagnetic data of Hangzhou site (HZT) which is located about 100 km northeast of Shuanglong 500 kV substation and Quanzhou site (QZH) which is located about 150 km southwest of Lili 500 kV substation are used. The distances between the substations and the magnetometer sites are sufficiently short to determine the geoelectric fields at the GIC-monitoring sites. A uniform Earth’s conductivity model with conductivity of 0.0001 S/m was adopted for both of the two substations as the geologic structure of the two sites are similar. Then on the basis of equation (3) we obtain parameters a and b for the two GIC sites by fitting the recorded GIC and calculated electric field at each site. Geomagnetic variation, geoelectric field, measured and the fitted GIC for the Shuanglong and LiLi 500 kV substations are shown in Figures 5 and 6, respectively. Because we focus on the GIC due to the SSC, the data shown in the figures just cover the scope of ZHANG ET AL.

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Figure 6. The x and y components of the geomagnetic variation observed at QZH, the x and y components of geoelectric field, and the measured (black curve) and fitted (red curve) GIC at Lili 500 kV substation during the SSC event of 17 March 2015.

the SSC event. From the top to bottom, Figure 5 shows the x and y components of the geomagnetic variation observed at HZT, the x and y components of calculated electric field, and measured and fitted GIC at Shuanglong substation; the black curve denotes the observed GIC, and the red curve indicates the fitted one. The cross-correlation coefficient (cc) and normalized root-mean-square (nRMS) difference between the observed and fitted GIC are labeled on the right axis of the last panel. We get a = 8.33 Akm/V, b = −5.76 Akm/V for Shuanglong 500 kV substation with a cross-correlation coefficient of 0.94, and the nRMS difference of 0.32. The format of Figure 6 is the same as Figure 5. The a and b coefficients for Lili 500 kV substation are −0.59 Akm/V and −6.16 Akm/V, and the cross-correlation coefficient is 0.94, and nRMS difference is 0.37. The two substations are not far from each other, and the geologic structures are similar, so the geomagnetic variation and electric field at the two sites are very similar. However, the GIC signal recorded at Shuanglong substation is stronger and the impulse due to the SSC is narrower than Lili substation; this is mainly attributable to different topology and electrical properties of the two networks. The parameters a for the two sites are very different; the a for the Shuanglong substation is much larger than that for Lili substation, which implies the GIC at Shuanglong is more dependent on Ex than Lili.

4. Simulation of GIC We run a global MHD model to simulate the GIC at Shuanglong and Lili 500 kV substations due to the SSC event of 17 March 2015 with the in situ solar wind plasma and interplanetary magnetic field data as input. Simulation results and recording GIC at the two sites will be compared to estimate the capability of the model in reproducing the GIC at low-latitude locations. 4.1. The Model and Method In this study, the modeling process is performed using a global MHD code, the so-called piecewise parabolic method with a Lagrangian remap (PPMLR)-MHD code which is developed by Hu et al. [2007]. ZHANG ET AL.

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Figure 7. The solar wind and IMF conditions during the interplanetaray shock on 17 March 2015 observed by the Wind spacecraft. From the top to bottom, they are the IMF (total magnitude B, By , and Bz ); three components of the solar wind velocity Vx , Vy , and Vz ; the solar wind plasma number density N; and the temperature T .

The code has been successfully applied in modeling various dynamical processes in the interaction of the solar wind with the magnetosphere [Wang et al., 2013]. It solves ideal MHD equations for the solar wind-magnetosphere-ionosphere coupling system. The solution domain extends from 30 RE to −300 RE along the x direction, and from −150 RE to 150 RE in the y and z directions with the smallest grid spacing of 0.4 RE in the GSM coordinates in this study. The inner boundary is taken at 3 RE centered on the Earth and an electrostatic ionosphere is set at 110 km (1.017 RE ). Along the Earth’s dipole magnetic field lines, the field-aligned currents (FACs) are mapped from the inner boundary to the ionosphere where they are used as source term of a two-dimensional Poisson equation for electric potential. The potential of the ionosphere is then mapped back to the inner boundary to obtain convection velocity. An inhomogeneous ionospheric conductivity model is utilized, in which two models are applied together. For the contribution from the solar EUV radiation, a model in which the conductance depends on the solar flux F10.7 and solar zenith angle 𝜒 [Moen and Brekke, 1993]. For the auroral region, the model developed by Ahn et al. [1998] is used, in which the conductance is empirically derived from the geomagnetic disturbance. Detailed information about the ionospheric conductivity model can be found in Wang et al. [2011]. At the inflow boundary the model is driven by the solar wind and IMF observations from the Wind spacecraft (available at http://cdaweb.gsfc.nasa.gov/istp_public/) in this study. On 17 March 2015 the Wind spacecraft observed an interplanetary shock at 03:59 UT with sudden increase of the magnetic field, solar wind velocity, plasma density, and temperature as Figure 7 shows, which caused the geomagnetic SSC event observed at 04:44 UT on the ground. From the top to bottom, Figure 7 shows the total magnitude of IMF B and y and z components By and Bz ; three components of the solar wind velocity Vx , Vy , and Vz ; solar wind plasma number density N; and the temperature T . In order to keep the divergence-free condition in MHD model, IMF Bx is fixed to 0 in the simulation. According to the solar wind velocity, the solar wind and IMF data are shifted 40 min from the location of the Wind satellite to the inflow boundary of the PPMLR-MHD model. We calculate the geomagnetic variations at the two substations by integrating the space current systems including the magnetosphere currents, ionosphere currents, and FAC at the gap region. The detailed method for calculating the FAC at the gap region and the integral process can be found in Zhang et al. [2013]. In addition, we take the contribution of the internal currents which is roughly one third of total geomagnetic perturbation into account [Häkkinen et al., 2002]. Based on the geomagnetic variation, the geoelectric field is ZHANG ET AL.

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Figure 8. Simulation (red) and observation (black) results of the geomagnetic variation, time derivative of the geomagnetic variation, geoelectric field, and GIC at Shuanglong 500 kV substation.

computed according to equation (2), and then GICs are obtained by using equation (3) and the parameters determined in section 3. 4.2. Simulation Results The simulation results for Shuanglong 500 kV substation are shown in Figure 8, from the top to bottom, the figure shows the horizontal geomagnetic variation Bx and By , the time derivative of the geomagnetic variation dBx ∕dt and dBy ∕dt, the geoelectric field Ex and Ey , and GIC. Simulation results are illustrated by red curves; for comparison we also plot in the figure the observations with black curves. The cross-correlation coefficient and nRMS difference are labeled at the right of each panel. As is seen from the figure, the model captures the Bx fluctuations very well with high cross-correlation coefficient of 0.93 and low nRMS difference of 0.32; the related dBx ∕dt and Ey fluctuations are well reproduced too. Moreover, the peak values of Bx , dBx ∕dt, and Ey which appear at about 04:45 UT produced from the model are very close to the observed peaks. Large deviation exists between the modeled and observed By , related dBy ∕dt, and Ex . Typically, the variation of By is much weaker than Bx at low-latitude locations. Though GIC at Shuanglong depends strongly on Ex as the parameter a is 8.33 V/km, the model still captures the main feature of the GIC measurement with cross-correlation coefficient of 0.62 and reproduces the amplitude of the peak to a degree. ZHANG ET AL.

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Figure 9. Simulation (red) and observation (black) results of the geomagnetic variation, time derivative of the geomagnetic variation, geoelectric field, and GIC at Lili 500 kV substation.

Figure 9 presents the model results for Lili 500 kV substation in the same format as Figure 8. The observation of Bx , By and dBx ∕dt, dBy ∕dt at the magnetometer site QZH, calculated geoelectric field based on the observed geomagnetic variation, and measured GIC are plotted with black curves to compare. The results indicate that the model reproduces Bx , dBx ∕dt, and Ey with high cross-correlation coefficients, and the modeled peak values are very close to the observed ones. Again, the model misses the basic trend of By , dBy ∕dt, and Ex , similar to Shuanglong. GIC at Lili is GIC(A) = −0.59Ex − 6.16Ey , meaning the GIC does not rely strongly on Ex which is closely related to By . This is different from Shuanglong for which parameter a is much larger. At Lili 500 kV substation, the model not only captures the variation tendency of the GIC signal with cross-correlation coefficient of 0.63 but also reproduces the amplitude of the peak value about 3 A at 04:45 UT. The model generally reproduces the geomagnetic variations and captures the main feature of the GIC signals at the two GIC-monitoring sites. The results also indicate that the northward component of geomagnetic variation Bx and eastward component of electric field Ey are dominant components at low-latitude locations during SSC events. ZHANG ET AL.

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Figure 10. The GIC and geomagnetic variation at Ling’ao nuclear power plant and SYM-H index during the storm of 9–10 November 2004.

5. Discussion The GIC problems at middle- and low-latitude power networks have increasingly gained attention with the increasingly large scale power networks. The magnetopause current, ring current, and equatorial electrojet are considered as possible sources of geomagnetic variations responsible for GIC hazards to the networks. In this study, we reported the GIC monitored at two low-latitude Chinese substations during the largest storm of current solar cycle so far. The observed GICs due to the SSC prior to the storm are 2–3 times of the peak value during main phase. To our knowledge, this phenomenon has not been reported in the literature. Further analysis based on GIC index indicated that the phenomenon is more likely to happen at low-latitude locations. This underlines that the SSC which is associate mainly with the magnetopause current is important in inducing GIC at low-latitude power networks; although the GICs recorded at Shuanglong and Lili 500 kV substations during this large storm are not very high, the peak values are 4.5 A and 2.8 A, respectively, caused by the SSC, and did not influence the operation of the power systems. We note a documented GIC event which was measured at the Ling’ao nuclear plant (geographic latitude ∼23∘ , geomagnetic latitude ∼12∘ ) in the Guangdong province of China during the large storm of 9–10 November 2004 [Liu et al., 2009]; the intensity of the storm was similar to that investigated in this study, and the change rate of the SSC at Ling’ao was also close with that at Shuanglong and Lili. We plot the observed GIC, geomagnetic perturbation at the nearest magnetometer site ZQT (the location of ZQT can be found in Figure 2) and SYM-H index in Figure 10. As shown in the figure, GIC measured at Ling’ao was much higher than that recorded at Shuanglong and Lili in this paper. The GIC due to the SSC that happened at 18:50 UT on 9 November 2004 was −55.8 A. So there is a big difference for GIC at different power systems under similar geomagnetic variation. In fact, the Earth’s conductivity at Ling’ao is higher than Shuanglong and Lili; thus, the same geomagnetic perturbation would induce weaker geoelectric field at Ling’ao according to equation (2). Thus, the large GIC measured at Ling’ao is mainly attributed to the topology and electric properties of the power network. Liu et al. [2009] determined that the parameters a and b are −3.5 and −256.2 Akm/V for Ling’ao power network with the uniform ground conductivity of 0.0008 S/m. We estimate the GIC would be tens of amperes if they are monitored at Ling’ao during the large storm of 17 March 2015 and under the assumption that there are no changes that occur in the network during these years. Small GIC values recorded at Shuanglong and Lili during the big storm indicated the electric parameters and topology of the power system make the networks less vulnerable to large GIC attacks. To predict the GIC caused by the SSC event at the two Chinese substations, we carried out simulations by running the PPMLR-MHD code with the observation of Wind satellite as input. The model generally captured the features of GIC signal, which implied that the MHD model such as the PPMLR-MHD code is promising in providing short lead time GIC predictions for low-latitude power grids during the SSC events. The lead time depends upon the speed of the solar wind shock and the computational capacity available for model execution. In the PPMLR-MHD model, the simulation grid was 160(x ) × 162(y) × 162(z) points with the smallest grid spacing of 0.4 RE , and the ionospheric grid was 1∘ (latitude) × 3∘ (longitude) covering the high-latitude region ZHANG ET AL.

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down to a magnetic colatitudes of 37∘ . The model took 36 min (calculation time) for the shock from the inflow boundary at 30 RE to the end of the GIC impulse due to the SSC using 36 processors. The prediction lead time would be 9 min since the travel time of the shock from the satellite to Earth was about 45 min. Basically, the key point of the model’s capability in GIC prediction lies in whether or not the model can reproduce realistic geomagnetic variation at the site of interest from which the GIC is derived at last. The model reproduced the dominant component Bx of the geomagnetic perturbation at the two GIC-monitoring sites with very high cross-correlation coefficient of more than 0.9 and very low nRMS difference, and especially a correct behavior of dBx ∕dt from which a satisfactory estimation of GIC can be obtained. In the model, the field-aligned currents are the main contributor of By at low-latitude locations; the lack of realistic inner magnetosphere model in current PPMLR-MHD model might be the reason of poor performance in reproducing By . By due to the SSC event is relatively weaker at low latitude, but accurate prediction of By could improve the precision of the model in forecasting GIC more or less. Besides the SSC event presented in this paper, five other SI/SSC events have been simulated. The model also reproduced the geomagnetic variations and dB∕dt at low latitudes for those events (not shown). The ring current and equatorial electrojet could also produce large GIC to power networks at low latitudes. However, the simple magnetosphere-ionosphere coupling process and ionospheric model in current PPMLR-MHD model limits the capability of the model to predict these types of low-latitude events. While for the GIC events at high latitudes where ionospheric currents are the dominant contributor to the geomagnetic variations, the model has successfully simulated the GIC at a transformer of the Finnish high-voltage power system during the space weather event of 22–23 September 1999 [Zhang et al., 2012].

6. Summary

Acknowledgments We thank the North China Electric Power University for providing the monitored GIC data; to access the data, please contact the authors (e-mail: [email protected] and [email protected]). We acknowledge the use of geomagnetic data from the Chinese Meridian Project, Geomagnetic Network of China, and the INTERMAGNET. We also thank the World Data Center for Geomagnetism Kyoto for providing the AE index, SYM-H index, and the geomagnetic quietest days for data processing. Our acknowledgement also goes to CDAWeb of Goddard Space Flight Center for the use of the Wind satellite data. All the analyzed simulation data can be accessed by contacting the authors (e-mail: [email protected] and [email protected]). This work was supported by 973 Program2012CB825602, 863 Program2012AA2100, NNSFCgrants 41404123 and 5117745, and in part by the Specialized Research Fund for State Key Laboratories of China. The author J.J. Zhang is also supported by the Youth Innovation Promotion Association, Chinese Academy of Sciences.

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In summary, we reported an event in which the GIC due to SSC is 2 or 3 times higher than that during storm main phase. Further analysis about the GIC index derived from the geomagnetic observations implied that this phenomenon is more remarkable at low-latitude sites, which demonstrated the important role of the SSC events played in inducing GIC at low-latitude power systems. In addition, we ran the PPMLR-MHD model to predict the GIC caused by the SSC event of 17 March 2015 at the two Chinese GIC-monitoring sites by using the solar wind conditions observed by the Wind satellite as input. The model reproduced the main geomagnetic variations pretty well and generally captured the main feature of recorded GIC. This method will provide a short lead time GIC forecast for the low-latitude power networks when the upwind satellite observes an interplanetary shock. While this is the first try for the model to carry out forecast of GIC at low-latitude locations caused by the SSC event, rigorous validation of the model should be done as a next step for operational prediction.

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