Implementation of a GPU-Accelerated Particle Filter Algorithm for Simulation of Large-Scale Power Systems

Ariana Minot, Ohio Supercomputer Center

Usage Details

Advances in simulation and mathematical modeling for dynamic, stochastic systems are needed in order to better understand the behavior of the electric power grid under a high penetration of renewable energy sources and distributed smart loads. One key aspect of reliable power system operation is state estimation, which keeps track of the state of the grid by estimating the voltage magnitudes and phase angles at each bus. With the advent of phasor measurement units (PMUs), real-time measurements of electric waves at various locations on the grid using GPS satellite-synchronized clocks are available. Introducing PMUs leads to both an increase in the dimension of the data and in the sampling rate that will require using HPC resources. Parallelization of the matrix operations and numerical integration at the core of the state estimation problem must be studied. Recent work on dynamic state estimation shows promising results using particle lters to estimate dynamic state variables, such as the generator rotor angle and speed. Particle filters are a type of sequential Monte Carlo method that replace distributions by weighted samples. This allows for studying non-linear systems with non-Gaussian noise but comes at a higher computational cost. Minot will study the implementation of a GPU-accelerated particle filter algorithm for simulation of a large-scale power system in real-time using Blue Waters. She also will explore optimal placement of PMUS and the further development of distributed state estimation algorithms that do not require a central coordinator and can obtain a global overview of the state of the power system from local and neighboring estimates alone.