Abstract

On-line transient stability analysis of a power grid is crucial in determining whether the power grid will traverse to a steady state stable operating point after a disturbance. The transient stability analysis involves computing the solutions of the algebraic equations modeling the grid network and the ordinary differential equations modeling the dynamics of the electrical components like synchronous generators, exciters, governors, etc., of the grid in near real-time. In this research, we investigate the use of time-parallel approach in particular the Parareal algorithm implementation on Graphical Processing Unit using Compute Unified Device Architecture to compute solutions of ordinary differential equations. The numerical solution accuracy and computation time of the Parareal algorithm executing on the GPU are demonstrated on the single machine infinite bus test system. Two types of dynamic model of the single synchronous generator namely the classical and detailed models are studied. The numerical solutions of the ordinary differential equations computed by the Parareal algorithm are compared to that computed using the modified Euler’s method demonstrating the accuracy of the Parareal algorithm executing on GPU. Simulations are performed with varying numerical integration time steps, and the suitability of Parareal algorithm in computing near real-time solutions of ordinary different equations is presented. A speedup of 25× and 31× is achieved with the Parareal algorithm for classical and detailed dynamic models of the synchronous generator respectively compared to the sequential modified Euler’s method. The weak scaling efficiency of the Parareal algorithm when required to solve a large number of ordinary differential equations at each time step due to the increase in sequential computations and associated memory transfer latency between the CPU and GPU is discussed.

Highlights

  • The time-domain simulation technique is widely used by the power industry to describe a power grid transient behavior accurately

  • The transient stability analysis involves computing the solutions of the algebraic equations modeling the grid network and the ordinary differential equations modeling the dynamics of the electrical components like synchronous generators, exciters, governors, etc., of the grid in near real-time

  • We investigate the use of the time-parallel approach and in particular the Parareal algorithm (PRA) implementation on the Graphical Processor Unit (GPU) using the Compute Unified Device Architecture (CUDA) for solving ODEs representing the electrical components of the power system

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Summary

Introduction

The time-domain simulation technique is widely used by the power industry to describe a power grid transient behavior accurately. We investigate the use of the time-parallel approach and in particular the Parareal algorithm (PRA) implementation on the Graphical Processor Unit (GPU) using the Compute Unified Device Architecture (CUDA) for solving ODEs representing the electrical components of the power system. Three systems are used for testing the proposed approach The speedup they demonstrate by using only higher derivatives of the generators; the equations are decoupled from the power network equation, leading to spatial and time-domain parallel decomposition hybrid approach. The focus of the investigation is to develop a reliable implementation of PRA on CUDA architecture to solve ODEs in temporal decomposition to reduce computational time and which can be applied to achieve real-time or faster than real-time TSA with a large number of GPUs. This paper is organized as follows: In section 2, the PRA with the Predictor Correction approach is discussed.

Parareal Algorithm
Ordinary Differential Equations Representing the Power System Dynamics
Classical Model of Synchronous Generators
Detailed Model of Synchronous Generators
GPGPU Based Parareal Algorithm Implementation
Test System
Results and Performance
Simulations Using the Classical Generator Model
Simulations Using the Detailed Generator Model
Performance Analysis
Conclusion
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