State-Space Nodal (SSN): New challenges of 2014, by OPAL-RT

1. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
1
Christian Dufour, Ph.D.
OPAL-RT TECHNOLOGIES
christian.dufour@opal-rt.com
State-Space Nodal (SSN)
New challenges of 2014

2. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
2
• SSN solver in brief
• SSN usage in distribution grid applications
• SSN usage in More Electric Aircrafts.
• SSN usage in super-large fusion reactor converters.
• Iterative methods in SSN.
Outline

3. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
3
• SimPowerSystems (SPS) is the main blockset of Simulink to simulate electric circuits and
power systems
• ARTEMiS is a real-time enabler for SPS, its adds adapted real-time solvers like SSN (State-
Space Nodal)
Simulink
SimPowerSystems
Real-Time
ARTEMiS
eMEGAsim
ARTEMiS, SSN and SimPowerSystems

4. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
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State-space vs. ‘Nodal method’
• Directly find the state-space equations
(‘ABCD’ equations)
• not so easy to do in general, many
special cases exists (ex: state
dependency)
• 1st , make small pieces (or groups!)
of the network connected to nodes
• Find the equation of each one
• Ex: Capa: Vc=1/C*INT{i*dt}
• Solve the common
voltages/currents with nodal
admittance equation VY=I

5. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
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State-Space Nodal Solver (SSN)
• Reducing the node number is critical because the LU factorisation of Y matrix time is
proportional to the cube (O3) of the size of matrix.
• SSN allow user to select the node location and limit the number of nodes.
• Below is an example of this node reduction effect:
• In EMTP/RTDS/Hypersim: using the standard set of {R, RL, RLC, etc…} branches, we end up with 30 nodes.
• In SSN, the user choose the node location: in the case below, it produces only 1 node!
Comparison of node number for standard nodal admittance method and SSN
Using EMTP/RTDS/Hypersim: 10 nodes Using SSN: 1 node!

6. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
6
Simulation challenges of large power grids
• Grids represent a very large simulation problem
• Simulation can be parallelized by using the line propagation delays (-> maps
into inter-core delays!)
6-core eMEGAsimGrid model
1 2
3 4
5 6

7. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
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Simulation challenges of power systems
• So what do we do to parallelize the simulation when no transmission lines?
• Ex: distribution systems, on-board power systems…
Example of renewable integration into a distribution gridAircraft on-board power system

8. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
8
Parallel State-Space Nodal Solver (SSN)
• SSN allow the parallelisation of the network equations without delay.
• How is this possible? Reducing the node number has the effect of creating much bigger branch
equations. These SSN group equations are computed on many CPUs in parallel!
• Note that this would be possible in theory for standard EMTP algo but in would be practically
inefficient because of the inter-core communication costs for a large number of branches.
SSN groups computed mostly in parallel
LU solution is made
on a different core

9. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
9
Active distribution grid: Actual client cases
• These 2 results below are from actual clients.
• They used parallel SSN with 4 cores of 3.3 GHz i7 Intel PC, with absolutely no delay
or stubline in the network.
• Lilles configuration is published but not the ‘Italy client’.
• Both are radial networks like the image here:
Client Network nodes
(EMTP equivalent)
SSN nodes # of LC states Simulation time
step
Number of RTDS
racks required
Lilles L2EP 1 650 18 387 105µs np3
‘Italy client’ 2 740 9 981 70 µs np3
Lilles distribution grid configuration
3 np: Not Possible (even with a large number of RTDS racks)
Because no decoupling if possible and RTDS is limited to 72 nodes
per nodal solution
60µs (expected)

10. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
10
SSN in More-Electric-Aircraft (Bombardier Global Express)
• GLEX stats:
• 25 SSN groups
• 109 switches
• Y size: 48x48
• Time step 39 µs
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
14
16
18
20
22
24
26
28
30
Time (s)
Voltage(V
DC
)
SPS 1us
SPS 50us
SSN 50us
L. Montealegre Lobo, C. Dufour, J. Mahseredjian, “Real-time Simulation of More-Electric Aircraft Power Systems”, Proceedings
of the 15th European Conference on Power Electronics and Applications (EPE’13 ECCE Europe), Lille, France, Sept. 3-5, 2013

11. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
11
• Undisclosed client.
• Application: fusion reactor coupled coil drives.
• Complained about very slow simulation in SPS (1 day typ.)
• Stats: 121 SSN groups, 468 switches and 291 nodes (Y matrix 95% sparse)
• Also used some Rte-Drive buck converters.
• Results in 33-100X acceleration
depending on size of system.
Super-Large Converter applications (fusion reactor)
Coil current drive similar to the one discussed

12. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
12
• Iterative MOV and Iterative Switches under
development
• Available in off-line mode since Q1/2014
• Q3/2014 expected date for RT.
• Available in HYPERSIM in RT-mode since 2012.
Iterative methods in SSN (MOV and Switches)
C. Dufour, O. Tremblay, “Iterative Algorithms of Surge Arrester for Real-Time Simulators”, 18th Power Systems
Computation Conference (PSCC 2014), August 18-22, 2014, Wroclaw, Poland.

13. The 7th International Conference
on Real-Time Simulation Technologies
Montreal | 9-12 June, 2014
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• New challenges shows the existing and surprising capability of SSN
• New challenges required the coding of new features like iterations.
Conclusion