1

GPU-Based Parallel EMT Simulation

of Power Electronics Dominated

Power Systems

Yin Xu

Professor, School of Electrical Engineering

Beijing Jiaotong University

* Part of the work was done in collaboration with Prof. Ying Chen, Tsinghua University

Background2

AC/DC hybrid power gridLarge Scale Renewables DGs in Distribution Systems

Power Electronics are integrated into power systems, in terms of wind farms, solar

power plants, HVDC, FACTS, distributed generators.

Challenges in Simulation3

Dynamic Simulation (Electromechanical Transient Simulation) Hard to accurately simulate PE-related phenomena, such as commutation failure

of HVDCs and subsynchronous oscillation between WFs and the power grid

Transient Simulation (Electromagnetic Transient Simulation) Computationally expensive

Hybrid Simulation Interface

Computational efficiency is limited by the transient part for power systems with

large-scale PE-based components

Objective: Improve computational efficiency of transient simulation

Speedup Transient Simulation4

Dynamic Average-value modelling Parallel Computing

No need to simulate switching events

Accurate terminal transients

Suitable for parallel computing

Detailed model

Average-value model

Features of GPU:

Thousands of cores

Not suitable to deal with complicated procedure

Dynamic Average-Value Modelling5

Analytical Average-Value Modelling Parametric Average-Value Modelling*

Fully

controlled

Non-fully

controlled

av

bv

cvdci

Controlled source equivalent circuit

|| ||qds dcv v

|| ||dc qdsi i

0 01

p

i i i

i

dxA x B u A x B u S

dt

y Cx Du

3

0 0

1

i i i

i

d xA x B u A x B u d

dt

y Cx Du

Analytical derivation

Parameter extraction

Detailed Model

AC Voltage/current

Positive sequence

Negative sequence

DC

DQ component

*J. Jatskevich, et al. Parametric average-value model of synchronous machine-rectifier systems. IEEE Transactions on Energy Conversion, 2006.

Case Study 1: DFIG Wind Farms6

Detailed model

Average-value model

DC voltageAcceleration Ratio

Detailed model

Average-value model

Active Power

Wind speed

1.5 MW DFIGs

Step changes in wind speed

For efficiency test:

• Condition 1: identical wind speed

for all WTGs

• Condition 2: different wind speeds

for WTGs

Case Study 2: LCC-HVDC7

230kV345kV 500kV

Rec1

Rec2

Inv1

Inv2

2kA

Model Time cost Time steps Time step Acceleration Rate

Detailed 34.24s 80000 50us

PAVM 5.33s 8000 500us 6.42

Internal fault scenario

Short fault at T3 of Inv1

Duration: 4s

Fix time step

Fully GPU-Based EMTP-Type Solver 8

Heterogeneous Computation

• Control system: layered directed acyclic graph (LDAG)

Fully GPU-based

Minimized Communication between CPU / GPU

Less Kernels

Three kernels for all computations

Avoid Competition

Resource AllocationPre-defined LDAG operation flow

1

2

3

How? Homogeneous Computation

• Solve Norton Equivalent Currents

• Basic Fused Multiply-Add (FMA)

Instructions

Network Solution

• Sparse Direct Solver / Dense Matrix-Vector Multiplication

( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( ) ( )

ne

ne c

I t G t U t I t

I t P t I t t Q t U t t I t

(cuSOLVER) (cuBLAS)

Case Study 3: Large Scale Network9

10x speedups• for large-scale case

on a single GPU without network partition

• Test System Information• IEEE 123-Node System + PVs• Duplicate to create large systems• Largest system: 18176 nodes and 640 PVs

NVIDIA

Tesla K20x

Case Study 4: Performance on Different GPUs

10

• Base case information• IEEE 13-Node System + PVs and batteries

• Two Types of GPUs• K20x and P100

Case Study 5: DIFG Wind Farm11

• Speedups

Num. of DFIG

Spee

d-u

ps

NVIDIA

TeslaTM K20

• 50x acceleration• Not affected by simulation time step

A Cloud-Based Simulator: CloudPSS

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www.cloudpss.net

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Yin Xu, Professor

School of Electrical Engineering

Beijing Jiaotong University

Beijing 100044, China

xuyin@bjtu.edu.cn

Thanks for your attention!

mailto:xuyin@bjtu.edu.cn