Design Optimization of Power Transformer Based on Multilevel Genetic Algorithm
Fengqin Liu · Xi'an Jiaotong Daxue xuebao · 2009
An optimization method,multilevel genetic algorithm(MLGA),is presented.The advantages of dynamic encoding and adjustment of genetic operators in MLGA are analyzed.And improved crossover and mutation techniques in MLGA are proposed.The correlated variables,described as points,in individuals can be operated with crossover and mutation simultaneously.These techniques ensure that all the individuals are limited in effective search space.This MLGA is applied in optimization design of power transformer successfully.A comparison with single level ordinary GA shows that MLGA reduces the cost of transformer by 1.83% and the no-load loss by 1.01% with similar computational consumption.