Machine-Learning-Driven Matrix Ordering for Power Grid Analysis

Published

Conference Paper

© 2019 EDAA. A machine-learning-driven approach for matrix ordering is proposed for power grid analysis based on domain decomposition. It utilizes support vector machine or artificial neural network to learn a classifier to automatically choose the optimal ordering algorithm, thereby reducing the expense of solving the subdomain equations. Based on the feature selection considering sparse matrix properties, the proposed method achieves superior efficiency in runtime and memory usage over conventional methods, as demonstrated by industrial test cases.

Full Text

Duke Authors

Cited Authors

  • Cui, G; Yu, W; Li, X; Zeng, Z; Gu, B

Published Date

  • May 14, 2019

Published In

  • Proceedings of the 2019 Design, Automation and Test in Europe Conference and Exhibition, Date 2019

Start / End Page

  • 984 - 987

International Standard Book Number 13 (ISBN-13)

  • 9783981926323

Digital Object Identifier (DOI)

  • 10.23919/DATE.2019.8715086

Citation Source

  • Scopus