### Abstract

The aim of transmission expansion planning is to determine which right-of-way to construct new lines in order to meet the future load in the most economical way. This problem has been solved by the mathematical programming techniques, which require considerable computational efforts, or by successive planning based on sensitivity analysis, which find a single non-optimal solution. Although another method that has efficiency for combinatorial problems is the neuro-computing, this approach obtains poor solutions while it saves computational efforts. The most desirable approach for this planning problem can find many good solutions in reasonable time, because experts of planning will easily plan the economical and reliable expansion according to these solutions by compare with each other. This paper presents an approach for solving transmission expansion planning based on neuro-computing hybridized with genetic algorithm. This approach generates suitable initial states, which include past information, of neural networks utilizing genetic algorithm. Mingling neuro-computing and genetic algorithm, the proposed approach can find many good solutions in reasonable time making full use of their merits. Computational examples show the effectiveness of the proposed approach by comparison with conventional approaches.

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

Title of host publication | Proceedings of the IEEE Conference on Evolutionary Computation |

Place of Publication | Piscataway, NJ, United States |

Publisher | IEEE |

Pages | 126-131 |

Number of pages | 6 |

Volume | 1 |

Publication status | Published - 1995 |

Event | Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2) - Perth, Aust Duration: 1995 Nov 29 → 1995 Dec 1 |

### Other

Other | Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2) |
---|---|

City | Perth, Aust |

Period | 95/11/29 → 95/12/1 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the IEEE Conference on Evolutionary Computation*(Vol. 1, pp. 126-131). IEEE.