Machine-Learning-Driven Matrix Ordering for Power Grid Analysis
- Resource Type
- Conference
- Authors
- Cui, Ganqu; Yu, Wenjian; Li, Xin; Zeng, Zhiyu; Gu, Ben
- Source
- 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE) Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019. :984-987 Mar, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power grids
Sparse matrices
Support vector machines
Mathematical model
Training
Matrix decomposition
Machine learning algorithms
Classification
direct sparse solver
fill-reducing ordering
machine learning
power grid analysis.
- Language
- ISSN
- 1558-1101
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.