An Approach for Transmission Expansion Planning using Neuro-Computing Hybridized with Genetic Algorithm

Abstract

The aim of transmission expansion planning is to determine which right-of-way to construct new lines in order to meet the future forecasted 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. It is difficult to plan the economical and reliable expansion due to its discrete and combinatorial nature. Although another method that has efficiency for combina-torial 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.
This paper presents an approach for solving transmission expansion planning based on neuro-computing hybrid-ized 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 ap-proach 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.