AN APPROACH FOR TRANSMISSION EXPANSION PLANNING USING NEUROCOMPUTING HYBRIDIZED WITH A GENETIC ALGORITHM

The aim of transmission expansion planning is to determine which right-of-way to use when constructing new lines in order to meet a forecasted load in the most economical way. This problem has been solved previously by mathematical sensitivity analysis (which finds a single nonoptimal solution). It is difficult to plan for economical and reliable expansion due to its discrete and combinatorial nature. Although another method that has efficiency for combinatorial problems is neurocomputing, this approach saves computational efforts but obtains poor solutions. The most desirable approach for this planning problem is one in which many good solutions are found in reasonable time, because planning experts will then be able to plan economical and reliable expansion according to these solutions. This paper presents an approach for solving transmission expansion planning based on neurocomputing hybridized with a genetic algorithm. This approach generates suitable initial states, which include past information, of neural networks utilizing genetic algorithm. Mingling neurocomputing and a 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.