### Abstract

In our earlier papers, we investigated the parallelization and implementation of Gauss-Seidel (G-S) and Successive Overrelaxation (SOR) power flow analysis on shared memory (SM) and distributed (DM) machines. For the SOR case, constant acceleration factors obtained from experiments are used to speedup convergence. In this paper, we introduce a new adaptive nonlinear SOR (ANSOR) algorithm which uses an approximated optimal acceleration factor obtained during the iteration process. The algorithm is shown to be faster due to the significant reduction in the number of iterations, and to converge robustly under heavily-loaded conditions on large power systems. We also implement parallel and sequential versions of our ANSOR algorithm on the nCUBE2 machine, and show that our algorithm is competitive with the fast decoupled load flow (FDLF) algorithm. Moreover, the portability of the parallel ANSOR code is demonstrated by porting the code to the Intel iPSC/860 hypercube and the Paragon mesh MIMD machines. However, our new algorithm is not a panacea for all problems, as we demonstrate with an example from transient stability analysis.

Original language | English |
---|---|

Pages (from-to) | 84-91 |

Number of pages | 8 |

Journal | IEEE Symposium on Parallel and Distributed Processing - Proceedings |

Publication status | Published - 1 Dec 1994 |

Externally published | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

**Adaptive SOR algorithm and its parallel implementation for power system applications.** / Huang, Garng Morton; Ongsakul, W.

Research output: Contribution to journal › Conference article

}

TY - JOUR

T1 - Adaptive SOR algorithm and its parallel implementation for power system applications

AU - Huang, Garng Morton

AU - Ongsakul, W.

PY - 1994/12/1

Y1 - 1994/12/1

N2 - In our earlier papers, we investigated the parallelization and implementation of Gauss-Seidel (G-S) and Successive Overrelaxation (SOR) power flow analysis on shared memory (SM) and distributed (DM) machines. For the SOR case, constant acceleration factors obtained from experiments are used to speedup convergence. In this paper, we introduce a new adaptive nonlinear SOR (ANSOR) algorithm which uses an approximated optimal acceleration factor obtained during the iteration process. The algorithm is shown to be faster due to the significant reduction in the number of iterations, and to converge robustly under heavily-loaded conditions on large power systems. We also implement parallel and sequential versions of our ANSOR algorithm on the nCUBE2 machine, and show that our algorithm is competitive with the fast decoupled load flow (FDLF) algorithm. Moreover, the portability of the parallel ANSOR code is demonstrated by porting the code to the Intel iPSC/860 hypercube and the Paragon mesh MIMD machines. However, our new algorithm is not a panacea for all problems, as we demonstrate with an example from transient stability analysis.

AB - In our earlier papers, we investigated the parallelization and implementation of Gauss-Seidel (G-S) and Successive Overrelaxation (SOR) power flow analysis on shared memory (SM) and distributed (DM) machines. For the SOR case, constant acceleration factors obtained from experiments are used to speedup convergence. In this paper, we introduce a new adaptive nonlinear SOR (ANSOR) algorithm which uses an approximated optimal acceleration factor obtained during the iteration process. The algorithm is shown to be faster due to the significant reduction in the number of iterations, and to converge robustly under heavily-loaded conditions on large power systems. We also implement parallel and sequential versions of our ANSOR algorithm on the nCUBE2 machine, and show that our algorithm is competitive with the fast decoupled load flow (FDLF) algorithm. Moreover, the portability of the parallel ANSOR code is demonstrated by porting the code to the Intel iPSC/860 hypercube and the Paragon mesh MIMD machines. However, our new algorithm is not a panacea for all problems, as we demonstrate with an example from transient stability analysis.

UR - http://www.scopus.com/inward/record.url?scp=0028698271&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028698271&partnerID=8YFLogxK

M3 - Conference article

SP - 84

EP - 91

JO - IEEE Symposium on Parallel and Distributed Processing - Proceedings

JF - IEEE Symposium on Parallel and Distributed Processing - Proceedings

SN - 1063-6374

ER -