An aggregating based model order reduction method for power grids |
| |
| Affiliation: | 1. State Key Lab of ASIC & System, Microelectronics Dept., Fudan University, Shanghai, PR China;2. School of Mathematical Sciences, Fudan University, Shanghai, PR China;1. Department of Industrial Engineering (D.I.In.), University of Salerno, Fisciano, Salerno, Italy;2. Advanced System Technology-STMicroelectronics International N.V., Plan-les-Quates, Switzerland;3. Advanced System Technology-STMICROELECTRONICS, Agrate Brianza, Milano, Italy;1. Department of Mathematics, University of California, Davis, CA 95616, USA;2. Departamento de Matemáticas, Facultad de Ciencias, Universidad Autónoma de Madrid, 28049 - Madrid, Spain;3. Instituto de Ciencias Matemáticas (CSIC-UAM-UC3M-UCM), C/Nicolás Cabrera, 13-15, 28049 - Madrid, Spain;1. School of Electrical Engineering, KAIST, Daejeon 305-701, Korea;2. Synopsys Inc., Mountain View, CA 94043, USA;3. School of Electrical and Computer Engineering, UNIST, Ulsan 689-798, Korea;1. iPack Vinn Excellence Center, School of Information and Communication Technology, Royal Institute of Technology (KTH) Electrum 229, 164 40 Stockholm-Kista, Sweden;2. School of Information Science and Technology, Fudan University, Shanghai, China |
| |
| Abstract: | Simulation of power girds has become increasingly computationally expensive. In this paper, we propose a Model Order Reduction (MOR) method for power grids by extending the existing Aggregating based MOR (AMOR) method. In the proposed method, besides resistors and capacitors, current sources are also aggregated to improve MOR efficiency. Moreover, pre-partition and parallelization techniques are employed to decrease reduction time. Numerical results demonstrate that compared to original circuits, the scale of power grids is greatly reduced without much loss of accuracy. The reduced-order models are especially useful in the multiple simulations of different working modes or different environment corners. |
| |
| Keywords: | Power grid Model order reduction Aggregating based |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
